In this paper, we propose a method for estimating the credibility of the posted information from users. We incorporate this method in a system which recommends the route and destination using other user's posted information. Users can post information, and other users can refer to them. These information includes a picture, comment, genre, expiration date, and so on. The system displays these information on the map. Since posted information can include subjective information from various perspectives, we can't trust all of (...) the postings as they are. We propose and integrate two factors of credibility of posted information. First, credibility of voting by other users is determined based on the posting user's own credibility and other users' credibility who approved or disapproved her/his posting information. The other is credibility of geographic posting tendency of the users. This credibility is determined based on dispersion of posted information on the map. From experimental results, we concluded our method measures credibility of posted information validly. (shrink)

The exponential growth of algorithms has made establishing a trusted relationship between human and artificial intelligence increasingly important. Algorithm systems such as chatbots can play an important role in assessing a user’s credibility on algorithms. Unless users believe the chatbot’s information is credible, they are not likely to be willing to act on the recommendation. This study examines how literacy and user trust influence perceptions of chatbot information credibility. Results confirm that algorithmic literacy and users’ trust play a pivotal role (...) in how users form perceptions of the credibility of chatbot messages and recommendations. Insights on how user trust is related to credibility provide a useful perspective on the conceptualization of algorithmic credibility. Algorithmic information processing that has been identified provides better foundations for algorithm design and development and a stronger basis for the design of sense-making chatbot journalism. (shrink)

This study aims to identify the necessary strategies for the development of a sustainable financial system. For this purpose, a novel approach could be provided for soft computing with Gaussian-based fuzzy DEMATEL approach to understand the significant levels and impact-relation degrees of these criteria. For robustness check, this evaluation has also been performed for triangular and trapezoidal fuzzy sets. There are many novelties of this study. Firstly, computer science has a significant role in the decision-making process. Another specificity is that (...) the use of Gaussian numbers with fuzzy numbers is a new area. In addition, interval type-2 fuzzy sets also provide a significant advantage in decision-making under uncertainty. The results demonstrate that the size of the alternative financing, investment in efficient projects, and effective advertisement/promotions are the most important items to affect the performance of alternative banking system of emerging economies. Hence, it is recommended that the new banking applications should be applied by the state or the state can provide some incentives for foreign banks to invest in emerging economies, such as tax reduction. Another important point is that the financial intermediations should generate a component financial analysis team to examine the financial reports of the companies in detail so that the financial credibility of these companies can be evaluated more effectively. (shrink)

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system.

Defeasible argumentation has been applied successfully in several real-world domains in which it is necessary to handle incomplete and contradictory information. In recent years, there have been interesting attempts to carry out argumentation processes supported by massive repositories developing argumentative reasoning applications. One of such efforts builds arguments by retrieving information from relational databases using the DBI-DeLP framework; this article presents eDBI-DeLP, which extends the original DBI-DeLP framework by providing two novel aspects which refine the interaction between DeLP programs and (...) relational databases. First, we expand the expressiveness of dbi-delp programs by providing ways of controlling how the information in databases is recovered; this is done by introducing filters that enable an improved fine-grained control on the argumentation processes which become useful in applications, providing the semantics and the implementation of such filters. Second, we introduce an argument comparison criterion which can be adjusted at the level of literals to model particular features such as credibility and topic expertise, among others. These new tools can be particularly useful in environments such as medical diagnosis expert systems, decision support systems, or recommender systems based on argumentation, where datasets are often provided in the form of relational databases. (shrink)

Recommender systems assist users in receiving preferred or relevant services and information. Using such technology could be instrumental in addressing the lack of relevance digital mental health apps have to the user, a leading cause of low engagement. However, the use of recommender systems for digital mental health apps, particularly those driven by personal data and artificial intelligence, presents a range of ethical considerations. This paper focuses on considerations particular to the juncture of recommender systems and digital (...) mental health technologies. While separate bodies of work have focused on these two areas, to our knowledge, the intersection presented in this paper has not yet been examined. This paper identifies and discusses a set of advantages and ethical concerns related to incorporating recommender systems into the digital mental health ecosystem. Advantages of incorporating recommender systems into DMH apps are identified as a reduction in choice overload, improvement to the digital therapeutic alliance, and increased access to personal data & self-management. Ethical challenges identified are lack of explainability, complexities pertaining to the privacy/personalization trade-off and recommendation quality, and the control of app usage history data. These novel considerations will provide a greater understanding of how DMH apps can effectively and ethically implement recommender systems. (shrink)

The article brings to light the use of recommender systems as technologies of the self, complementing the observations in current literature regarding their employment as technologies of ‘soft’ power. User practices on the music recommendation website last.fm reveal that many users do not only utilize the website to receive guidance about music products but also to examine and transform an aspect of their self, i.e. their ‘music taste’. The capacity of assisting users in self-cultivation practices, however, is not unique (...) to last.fm but stem from certain properties shared by all recommendation systems. Furthermore, unlike other oft-studied digital/web technologies of the self which facilitate ‘self-publishing’ vis-à-vis virtual companions in social media, recommender algorithms themselves can act as ‘intimate experts’, accompanying users in their self-care practices. Thus, recommendation systems can facilitate both algorithmic control and creative self-transformation, which calls for a theorization of this new cultural medium as a space of tension. (shrink)

Recommender Systems (RecSys) have been around since the early days of the Internet, helping users navigate the vast ocean of information and the increasingly available options that have been available for us ever since. The range of tasks for which one could use a RecSys is expanding as the technical capabilities grow, with the disruption of Machine Learning representing a tipping point in this domain, as in many others. However, the increase of the technical capabilities of AI-powered RecSys did (...) not come with a thorough consideration of their ethical implications and, despite being a well-established technical domain, the potential impacts of RecSys on their users are still under-assessed. This paper aims at filling this gap in regards to one of the main impacts of RecSys: personal autonomy. We first describe how technology can affect human values and a suitable methodology to identify these effects and mitigate potential harms: Value Sensitive Design (VSD). We use VSD to carry out a conceptual investigation of personal autonomy in the context of a generic RecSys and draw on a nuanced account of procedural autonomy to focus on two components: competence and authenticity. We provide the results of our inquiry as a value hierarchy and apply it to the design of a speculative RecSys as an example. (shrink)

This open access contributed volume examines the ethical and legal foundations of (future) policies on recommender systems and offers a transdisciplinary approach to tackle important issues related to their development, use and integration into online eco-systems. This volume scrutinizes the values driving automated recommendations - what is important for an individual receiving the recommendation, the company on which that platform was received, and society at large might diverge. The volume addresses concerns about manipulation of individuals and risks for personal (...) autonomy. From a legal perspective, the volume offers a much-needed evaluation of regulatory needs and lawmakers’ answers in various legal disciplines. The focus is on European Union measures of platform regulation, consumer protection and anti-discrimination law. The volume will be of particular interest to the community of legal scholars dealing with platform regulation and algorithmic decision making. By including specific use cases, the volume also exposes pitfalls associated with current models of regulation. Beyond the juxtaposition of purely ethical and legal perspectives, the volume contains truly interdisciplinary work on various aspects of recommender systems. (shrink)

With the rapid development of the Internet, recommendation systems have received widespread attention as an effective way to solve information overload. Social tagging technology can both reflect users’ interests and describe the characteristics of the items themselves, making group recommendation thus becoming a recommendation technology in urgent demand nowadays. In traditional tag-based recommendation systems, the general processing method is to calculate the similarity and then rank the recommended items according to the similarity. Without considering the influence of continuous user behavior, (...) in this article, we propose a personalized recommendation algorithm based on social tags by combining the ideas of Markov chain and collaborative filtering. This algorithm splits the three-dimensional relationship of into two two-dimensional relationships of and. The user’s interest degree to the tags is calculated by the Markov chain model, and then the items corresponding to them are matched by the recommended tag set. The influence between tags is used to model the satisfaction of items based on the correlation between the tags contained in the matched items, and collaborative filtering is used to complete the sparse values when calculating the interest and satisfaction between user–tags and user–items to improve the accuracy of recommendations. The experiments show that in the publicly available dataset, the personalized recommendation algorithm proposed in this article has significantly improved in accuracy and recall rate compared with the existing algorithms. (shrink)

In recent years, social networks have made significant progress and the number of people who use them to communicate is increasing day by day. The vast amount of information available on social networks has led to the importance of using friend recommender systems to discover knowledge about future communications. It is challenging to choose the best machine learning approach to address the recommender system issue since there are several strategies with various benefits and drawbacks. In light of this, (...) a solution based on the stacking approach was put out in this study to provide a buddy recommendation system in social networks. Additionally, a decrease in system performance was caused by the large amount of information that was accessible and the inefficiency of some functions. To solve this problem, a particle swarm optimization algorithm to select the most efficient features was used in our proposed method. To learn the model in the objective function of the particle swarm algorithm, a hybrid system based on stacking is proposed. In this method, two random forests and Extreme Gradient Boosting had been used as the base classifiers. The results obtained from these base classifiers were used in the logistic regression algorithm, which has been applied sequentially. The suggested approach was able to effectively address this issue by combining the advantages of the applied strategies. The results of implementation and evaluation of the proposed system show the appropriate efficiency of this method compared with other studied techniques. (shrink)

Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS’ technical aspects allows us to shed light on the ethical risks and challenges they (...) might raise on individuals’ well-being as they age. Moreover, the study proposes a categorization, understanding, and possible preventive/mitigation actions for the elicited risks and challenges through rethinking the AI ethics core principle of autonomy. Finally, elaborating on autonomy-related ethical theories, the paper proposes an autonomy-based ethical framework and how it can foster the development of autonomy-enabling HRS for AA. (shrink)

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed (...) design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology. (shrink)

This article analyses the ethical aspects of multistakeholder recommendation systems (RSs). Following the most common approach in the literature, we assume a consequentialist framework to introduce the main concepts of multistakeholder recommendation. We then consider three research questions: who are the stakeholders in a RS? How are their interests taken into account when formulating a recommendation? And, what is the scientific paradigm underlying RSs? Our main finding is that multistakeholder RSs (MRSs) are designed and theorised, methodologically, according to neoclassical welfare (...) economics. We consider and reply to some methodological objections to MRSs on this basis, concluding that the multistakeholder approach offers the resources to understand the normative social dimension of RSs. (shrink)

With the deployment of information and communication technologies and the needs of data and information sharing within cities, smart city aims to provide value-added services to improve citizens’ quality of life. But, currently city planners/developers are faced with inadequate contextual information on the dimensions of smart city required to achieve a sustainable society. Therefore, in achieving sustainable society, there is need for stakeholders to make strategic decisions on how to implement smart city initiatives. Besides, it is required to specify the (...) smart city dimensions to be adopted in making cities smarter for sustainability attainment. But, only a few methods such as big data, internet of things, cloud computing, etc. have been employed to support smart city attainment. Thus, this study integrates case-based reasoning as an artificial intelligence technique to develop a recommender system towards promoting smart city planning. CBR provides suggestions on smart city dimensions to be adopted by city planners/decision-makers in making cities smarter and sustainable. Accordingly, survey data were collected from 115 respondents to evaluate the applicability of the implemented CBR recommender system in relation to how the system provides best practice recommendations and retaining of smart city initiatives. Results from descriptive and exploratory factor analyses suggest that the developed system is applicable in supporting smart city adoption. Besides, findings from this study are expected to provide valuable insights for practitioners to develop more practical strategies and for researchers to better understand smart city dimensions. (shrink)

YouTube has been implicated in the transformation of users into extremists and conspiracy theorists. The alleged mechanism for this radicalizing process is YouTube’s recommender system, which is optimized to amplify and promote clips that users are likely to watch through to the end. YouTube optimizes for watch-through for economic reasons: people who watch a video through to the end are likely to then watch the next recommended video as well, which means that more advertisements can be served to them. (...) This is a seemingly innocuous design choice, but it has a troubling side-effect. Critics of YouTube have alleged that the recommender system tends to recommend extremist content and conspiracy theories, as such videos are especially likely to capture and keep users’ attention. To date, the problem of radicalization via the YouTube recommender system has been a matter of speculation. The current study represents the first systematic, pre-registered attempt to establish whether and to what extent the recommender system tends to promote such content. We begin by contextualizing our study in the framework of technological seduction. Next, we explain our methodology. After that, we present our results, which are consistent with the radicalization hypothesis. Finally, we discuss our findings, as well as directions for future research and recommendations for users, industry, and policy-makers. (shrink)

Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. The model (...) consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation. (shrink)

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries’ data of the International Trade Network. A recommendation system is then used to identify the products that correspond to the production capacity of each individual country but are somehow overlooked by (...) the country. Then, we simulate the evolution of the country’s fitness if it would have followed the recommendations. The result of this work is the combination of these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries. (shrink)

In this paper, we propose an inter-personal information sharing model among individuals based on personalized recommendations. In the proposed model, we define an information resource as shared between people when both of them consider it important --- not merely when they both possess it. In other words, the model defines the importance of information resources based on personalized recommendations from identifiable acquaintances. The proposed method is based on a collaborative filtering system that focuses on evaluations from identifiable acquaintances. It utilizes (...) both user evaluations for documents and their contents. In other words, each user profile is represented as a matrix of credibility to the other users' evaluations on each domain of interests. We extended the content-based collaborative filtering method to distinguish other users to whom the documents should be recommended. We also applied a concept-based vector space model to represent the domain of interests instead of the previous method which represented them by a term-based vector space model. We introduce a personalized concept-base compiled from each user's information repository to improve the information retrieval in the user's environment. Furthermore, the concept-spaces change from user to user since they reflect the personalities of the users. Because of different concept-spaces, the similarity between a document and a user's interest varies for each user. As a result, a user receives recommendations from other users who have different view points, achieving inter-personal information sharing based on personalized recommendations. This paper also describes an experimental simulation of our information sharing model. In our laboratory, five participants accumulated a personal repository of e-mails and web pages from which they built their own concept-base. Then we estimated the user profiles according to personalized concept-bases and sets of documents which others evaluated. We simulated inter-personal recommendation based on the user profiles and evaluated the performance of the recommendation method by comparing the recommended documents to the result of the content-based collaborative filtering. (shrink)

Decisions are taken by humans very often during professional as well as leisure activities. It is particularly evident during surfing the Internet: selecting web sites to explore, choosing needed information in search engine results or deciding which product to buy in an on-line store. Recommender systems are electronic applications, the aim of which is to support humans in this decision making process. They are widely used in many applications: adaptive WWW servers, e-learning, music and video preferences, internet stores etc. (...) In on-line solutions, such as e-shops or libraries, the aim of recommendations is to show customers the products which they are probably interested in. As input data the following are taken: shopping basket archives, ratings of the products or servers log files.The article presents a solution of recommender system which helps users to select an interesting product. The system analyses data from other customers' ratings of the products. It uses clustering methods to find similarities among the users and proposed techniques to identify users' profiles. The system was implemented in Apache Mahout environment and tested on a movie database. Selected similarity measures are based on: Euclidean distance, cosine as well as correlation coefficient and loglikehood function. (shrink)

YouTube has been implicated in the transformation of users into extremists and conspiracy theorists. The alleged mechanism for this radicalizing process is YouTube’s recommender system, which is optimized to amplify and promote clips that users are likely to watch through to the end. YouTube optimizes for watch-through for economic reasons: people who watch a video through to the end are likely to then watch the next recommended video as well, which means that more advertisements can be served to them. (...) This is a seemingly innocuous design choice, but it has a troubling side-effect. Critics of YouTube have alleged that the recommender system tends to recommend extremist content and conspiracy theories, as such videos are especially likely to capture and keep users’ attention. To date, the problem of radicalization via the YouTube recommender system has been a matter of speculation. The current study represents the first systematic, pre-registered attempt to establish whether and to what extent the recommender system tends to promote such content. We begin by contextualizing our study in the framework of technological seduction. Next, we explain our methodology. After that, we present our results, which are consistent with the radicalization hypothesis. Finally, we discuss our findings, as well as directions for future research and recommendations for users, industry, and policy-makers. (shrink)

Recommender systems are recently developed computer-assisted tools that support social and informational needs of various communities and help users exploit huge amounts of data for making optimal decisions. In this study, we present a new recommender system for assessment and risk prediction in child welfare institutions in Israel. The system exploits a large diachronic repository of manually completed questionnaires on functioning of welfare institutions and proposes two different rule-based computational models. The system accepts users’ requests via a simple (...) graphical interface, calculates the institutions’ profiles according to user preferences, and presents assessment scores, trends and comparative analyses of the corresponding data using assorted visual aids. Based on the analysis, the system offers three different strategies for objective assessment of the institutions’ functioning and risks. Qualitative and quantitative evaluation of the system’s effectiveness and accuracy demonstrates that it substantially improves the assessment process of a welfare institution. Moreover, it provides an effective tool for objective large-scale analysis of the institution’s overall state and trends, which were previously based primarily on the institution supervisors’ subjective judgment and intuition. In addition, the proposed recommender system has great practical and social impact as it may help identify and avert potential problems, malfunctions, flaws, risks and even tragic incidents in child welfare institutions, as well as increase their overall functioning levels. As a result, as a long-term social implication, the system may also help reduce inequality and social gaps in the Israeli society. (shrink)

Recommendation systems (RSs) predict what the user likes and recommend it to them. While at the onset of RSs, the latter was designed to maximize the recommendation accuracy (i.e., accuracy was their only goal), nowadays many RSs models include diversity in recommendations (which thus is a further goal of RSs). In the computer science community, the introduction of diversity in RSs is justified mainly through economic reasons: diversity increases user satisfaction and, in niche markets, profits.I contend that, first, the economic (...) justification of diversity in RSs risks reducing it to an empirical matter of preference; second, diversity is ethically relevant as it supports two autonomy rights of the user: the right to an open present and the right to be treated as an individual. So far, diversity in RSs has been morally defended only in the case of RSs of news and scholarly content: diversity is held to have a depolarizing effect in a democratic society and the scientific community and make the users more autonomous in their news choices. I provide a justification of diversity in RSs that embraces all kinds of RSs (i.e., a holistic moral defense) and is based on a normative principle founded on the agency of the user, which I call the right to be an exception to predictions. Such a right holds that the proper treatment of a RS user qua agent forbids providing them with recommendations based only on their past or similar users’ choices. (shrink)

Feasting on a plethora of social media platforms, news aggregators, and online marketplaces, recommender systems (RSs) are spreading pervasively throughout our daily online activities. Over the years, a host of ethical issues have been associated with the diffusion of RSs and the tracking and monitoring of users’ data. Here, we focus on the impact RSs may have on personal autonomy as the most elusive among the often-cited sources of grievance and public outcry. On the grounds of a philosophically nuanced (...) notion of autonomy, we illustrate three specific reasons why RSs may limit or compromise it: the threat of manipulation and deception associated with RSs; the RSs’ power to reshape users’ personal identity; the impact of RSs on knowledge and critical thinking. In our view, however, notwithstanding these legitimate concerns, RSs may effectively help users to navigate an otherwise overwhelming landscape. Our perspective, therefore, is not to be intended as a bulwark to protect the _status quo_ but as an invitation to carefully weigh these aspects in the design of ethically oriented RSs. (shrink)

I aim to illustrate how the recommender systems of digital platforms create a particularly problematic kind of vulnerability in their users. Specifically, through theories of scaffolded cognition and scaffolded affectivity, I argue that a digital platform’s recommender system is a cognitive and affective artifact that fulfills different functions for the platform’s users and its designers. While it acts as a content provider and facilitator of cognitive, affective and decision-making processes for users, it also provides a continuous and detailed (...) amount of information to platform designers regarding users’ cognitive and affective processes. This dynamic, I argue, engenders a kind of vulnerability in platform users, structuring a power imbalance between designers and users. This occurs because the recommender system can not only gather data on users’ cognitive and affective processes, but also affects them in an unprecedentedly economic and capillary manner. By examining one instance of ethically problematic practice from Facebook, I specifically argue that rather than being a tool for manipulating or exploiting people, digital platforms, especially by their underlying recommender systems, can single out and tamper with specific cognitive and affective processes as a tool specifically designed for mind invasion. I conclude by reflecting how the understanding of such AI systems as tools for mind invasion highlights some merits and shortcomings of the AI Act with regards to the protection of vulnerable people. (shrink)

There are many studies conducted on recommendation systems, most of which are focused on recommending items to users and vice versa. Nowadays, social networks are complicated due to carrying vast arrays of data about individuals and organizations. In today’s competitive environment, companies face two significant problems: supplying resources and attracting new customers. Even the concept of supply-chain management in a virtual environment is changed. In this article, we propose a new and innovative combination approach to recommend organizational people in social (...) networks based on organizational communication and SCM. The proposed approach uses a hybrid strategy that combines basic collaborative filtering and demographic recommendation systems, using data mining, artificial neural networks, and fuzzy techniques. The results of experiments and evaluations based on a real dataset collected from the LinkedIn social network showed that the hybrid recommendation system has higher accuracy and speed than other essential methods, even substantially has eliminated the fundamental problems with such systems, such as cold start, scalability, diversity, and serendipity. (shrink)

Recommender system depends on the thoughts of numerous users to predict the favourites of potential consumers. RS is vulnerable to malicious information. Unsuitable products can be offered to the user by injecting a few unscrupulous “shilling” profiles like push and nuke attacks into the RS. Injection of these attacks results in the wrong recommendation for a product. The aim of this research is to develop a framework that can be widely utilized to make excellent recommendations for sales growth. This (...) study uses the methodology that presents an enhanced clustering algorithm named as modified density peak clustering algorithm on the consumer review dataset to ensure a well-formed cluster. An improved recurrent neural network algorithm is proposed to detect these attacks in hybrid RS, which uses the content-based RS and collaborative filtering RS. The results are compared with other state of the art algorithms. The proposed method is more suitable for E-commerce applications where the number of customers and products grows rapidly. (shrink)

A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation, namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters (...) for the personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for recommendations. The proposed APHPRS system alleviates problems such as sparsity and cold-start problems. The use of affinity propagation and the hybrid recommendation technique used in the proposed approach helps in improving results against sparsity. Experiments reveal that the proposed APHPRS performs better than most of the existing recommender systems. (shrink)

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The (...) experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems. (shrink)

In this paper, we study the problem of protecting privacy in recommender systems. We focus on protecting the items rated by users and propose a novel privacy-preserving matrix factorization algorithm. In our algorithm, the user will submit a fake gradient to make the central server not able to distinguish which items are selected by the user. We make the Kullback–Leibler distance between the real and fake gradient distributions to be small thus hard to be distinguished. Using theories and experiments, (...) we show that our algorithm can be reduced to a time-delay SGD, which can be proved to have a good convergence so that the accuracy will not decline. Our algorithm achieves a good tradeoff between the privacy and accuracy. (shrink)

The purpose is to provide researchers with reliable Scientific Research Data from the massive amounts of research data to establish a sustainable Scientific Research environment. Specifically, the present work proposes establishing an Intelligent Recommendation System based on Machine Learning algorithm and SRD. Firstly, the IRS is established over ML technology. Then, based on user Psychology and Collaborative Filtering recommendation algorithm, a hybrid algorithm [namely, Content-Based Recommendation-Collaborative Filtering ] is established to improve the utilization efficiency of SRD and Sustainable Development of (...) SR. Consequently, the present work designs literature and SRD-targeted IRS using the hybrid recommendation under the background of SD. The proposed system’s feasibility is analyzed through experiments. Additionally, the system performance is analyzed and verified from accuracy, diversity, coverage, novelty, and recommendation efficiency. The results show that the hybrid algorithm can make up for the shortcomings of a single algorithm and improve the recommendation efficiency. Experiments show that the accuracy of the proposed CBR-CF algorithm is the highest. In particular, the recommendation accuracy for the single-user system can reach 82–93%, and the recall of all recommended algorithms falls between 60 and 91%. The recall of the hybrid algorithm is higher than that of a single algorithm, and the highest recall is 91%. Meanwhile, the hybrid algorithm has comprehensive coverage, good applicability, and diversity. Therefore, SD-oriented SRD-targeted IRS is of great significance to improve the SRD utilization and the accuracy of IRS, and expand the achievement value of SR. The research content provides a reference for establishing a sustainable SR environment and improving SR efficiency. (shrink)

In the contemporary digital age, recommender systems (RSs) play a fundamental role in managing information on online platforms: from social media to e-commerce, from travels to cultural consumptions, automated recommendations influence the everyday choices of users at an unprecedented scale. RSs are trained on users’ data to make targeted suggestions to individuals according to their expected preference, but their ultimate impact concerns all the multiple stakeholders involved in the recommendation process. Therefore, whilst RSs are useful to reduce information overload, (...) their deployment comes with significant ethical challenges, which are still largely unaddressed because of proprietary constraints and regulatory gaps that limit the effects of standard approaches to explainability and transparency. In this context, I address the ethical and social implications of automated recommendations by proposing a pro-ethical design framework aimed at reorienting the influence of RSs towards societal interest. In particular, after highlighting the problem of explanation for RSs, I discuss the application of beneficent informational nudging to the case of conversational recommender systems (CRSs), which rely on user-system dialogic interactions. Subsequently, through a comparison with standard recommendations, I outline the incentives for platforms and providers in adopting this approach and its benefits for both individual users and society. (shrink)

Up to 70% of all watch time on YouTube is due to the suggested content of its recommender system. This system has been found, by virtue of its design, to be promoting conspiratorial content. In this paper, I first critique the value neutrality thesis regarding technology, showing it to be philosophically untenable. This means that technological artefacts can influence what people come to value (or perhaps even embody values themselves) and change the moral evaluation of an action. Second, I (...) introduces the concept of an affordance, borrowed from the literature on ecological psychology. This concept allows me to make salient how technologies come to solicit certain kinds of actions from users, making such actions more or less likely, and in this way influencing the kinds of things one comes to value. Third, I critically assesses the results of a study by Alfano et al. I make use of the literature on affordances, introduced earlier, to shed light on how these technological systems come to mediate our perception of the world and influence action. (shrink)

The significance of big data for driving health research and improvements in patient care is well recognised. Along with these potential benefits, however, come significant challenges, including those concerning the sharing and linkage of health and social care records. Recently, there has been a shift in attention towards a paradigm of data sharing centred on the ‘trusted research environment’ (TRE). TREs are being widely adopted by the UK’s health data initiatives including Health Data Research UK (HDR UK),1 Our Future Health2 (...) and Genomics England.3 A recent review commissioned by the UK’s Department of Health and Social Care (hereafter referred to as the ‘Goldacre Review’) places TREs at the heart of its recommendations around the future of National Health Service (NHS) health data sharing for research, describing them as the ‘clear path forward’ to a health data system in which trust is ‘earned’ through ‘provable, credible steps to protect patient privacy, and by being transparent with everyone about everything that is done with their deepest medical secrets’.4 We argue that rather than building public trust, the TRE model actually reduces the need for trust in the use and sharing of patient health data. This is because trust is importantly connected to vulnerability and uncertainty; an essential part of trusting someone is accepting that one’s trust could be disappointed or betrayed. In attempting to provide assurances or guarantees of data privacy and security, TREs strive to remove this vulnerability, and so remove the need for trust. We do not see this as a problem and are broadly supportive of this kind of data sharing model because of the increased security and oversight it provides. However, having argued that TREs are not actually concerned with trust, we consider the importance of being precise about the words that we use …. (shrink)

GIS is important equipment in the substation system in a complex microgrid network. Due to the long-term service in harsh operation environments, the electrical performance of GIS equipment will be seriously affected. Therefore, the regular maintenance of GIS equipment in the substation system is a routine task so as to ensure the normal operation of the microgrid networks. The traditional method relies on manual labor, where not only with the low operation efficiency but also regarding some maintenance, labors cannot reach (...) the inside of the GIS equipment, which seriously restricted the completion of GIS equipment maintenance operation. Based on the above analysis, a basic configuration of a GIS equipment maintenance robot with smart structure, convenient control, and stable motion characteristic has been proposed in this paper. Through the optimization design of the chassis adsorption system, the GIS inspection robot can achieve hanging and vertical adsorption inside the GIS equipment and some special motion status. In this way, the entire GIS equipment can be inspected without blind spots. Through establishing the mechanical model of the GIS inspection robot under the hanging and vertical motion state, the robustness and stability of the GIS inspection robot in the special status have been analyzed, and the corresponding GIS inspection robot robust stability motion control method has also been proposed. Finally, through the integrated design of mechanical system and hardware and software control system, the GIS inspection robot physical prototype system has been developed and the maintenance operation experiment has been carried out in Hunan Electric Power Company Maintenance Company through simulating the internal GIS equipment. The physical prototype can realize the basic movement and special movement in internal GIS equipment without any blind spots and complete the inspection task of GIS equipment operation; the movement of GIS robot is dexterous and stable in the whole operation process, which has strong robust stability. The research of GIS inspection robot in the complex microgrid networks has important theoretical significance and practical application value for the intelligent operation and maintenance management of substation system in complex microgrid networks. (shrink)

Collaborative filtering is being used within organizations and in community contexts for knowledge management and decision support as well as the facilitation of interactions among individuals. This article analyzes rhetorical and technical efforts to establish trust in the constructions of individual opinions, reputations, and tastes provided by these systems. These initiatives have some important parallels with early efforts to support quantitative opinion polling and construct the notion of “public opinion.” The article explores specific ways to increase trust in these systems, (...) albeit a “guarded trust” in which individuals actively seek information about system foibles and analyze the reputations of participants. (shrink)

This paper proposes the use of an argumentation framework with recursive attacks to address a trust model in a collaborative open multi-agent system. Our approach is focused on scenarios where agents share information about the credibility (informational trust) they have assigned to their peers. We will represent informants’ credibility through credibility objects which will include not only trust information but also the informant source. This leads to a recursive setting where the reliability of certain credibility information depends on the credibility (...) of other pieces of information that should be subject to the same analysis. Credibility objects are maintained in a credibility base which can have information in conflict. In this scenario, we will formally show that our proposal will produce a partially ordered credibility relation; such relation contains the information that can be justified by an argumentation process. (shrink)

This study aims to determine the opinions of teachers of Religious Culture and Ethics Course (DKAB) about subject-based classroom application in-depth. The research has been carried from qualitative research methods with a case study design. In order to determine the working group of the study, criteria sampling was used in the first stage, and the maximum diversity sampling method was used in the next step. The sample of this research consists of 8 DKAB teachers working in Ankara province. A semi-structured (...) interview form has been used as a data collection tool in the study. The data of the qualitative of this research has been analyzed with both descriptive and content analysis by NVivo 10 program. In order to ensure validity and reliability in the study, credibility, transferability, consistency, and verifiability strategies have been used. When the research data were analyzed, four themes were formed: positive opinions about the application, negative opinions and problems related to the application, contribution of the practice to religious education and training, and recommendations about the application. DKAB teachers find the application more positive than the classroom system. It has been found that the practice of subject-based classrooms is a motivational tool for Primary School students, while it causes some problems for lower secondary school and upper secondary school students.Summary: Learning environments are among the key elements in the acquisition of 21st-century skills. Classrooms are also a part of the learning environment. Classes are the learning environment in which teachers and students spend most of their education and training process. In this context, subject-based classroom application is one of the preferred methods in the arrangement of classrooms.Subject-based classroom application means that the teachers have a special classroom related to their branches in the school building, and students attend the teacher's classroom according to the syllabus. In this practice, the teacher can decorate walls, floor, ceiling, column, and beam of the classroom with students’ study materials such as banner, poster, painting, cartoon to make the course more effective and efficient and to create a psychological atmosphere called classroom atmosphere-air.This application aims to activate the student by enriching the classroom and make learning effective and efficient. The classroom equipped with appropriate equipment and materials provides a favorable environment for the implementation of effective teaching strategies. The application can create “class/classroom culture” by using ordinary meanings and symbols related to the course. It provides students the opportunity to exhibit their products and contributes to students' self-regulation skills development. Besides, real-life practices or practices close to reality can be brought into the classroom. As a result, permanent learning can be realized with the possibility of creating a shared living space and the number of sensory organs participating in learning.This current study aims to address in-depth information about the opinions of Religious Culture and Moral Ethics (DKAB) teachers in primary, secondary, and high schools regarding the subject-based classroom application. When the studies in the literature are examined, it is seen that the studies related to the subject-based classroom application have increased since 2010. However, it is clear that this research is directed towards social studies, geography, science, and mathematics courses and is based on the opinions of students, teachers, and administrators at the secondary school level. In the context of DKAB course, there has been no research including the views of teachers, students, or school administrators regarding the application of subject-based classrooms. This study is carried out revealing perspectives of DKAB teachers by examining their opinions on the subject-based classroom application and contribute to the field. The research was conducted with a case study design from the qualitative research methods. The participants consist of 8 DKAB teachers working in Ankara province. A mixed purposive sampling method was used to determine the study group. In the first stage, the teachers implementing the DKAB classrooms application were taken as criteria. In the next step, the study group was determined within the framework of the maximum diversity sampling method by considering the variables such as school level, gender, age, education level, duration of teachers' work. The data were collected through a semi-structured interview form with expert opinion. In the form, there are nine main questions and probe questions, which include questions related to subject-based classrooms application, the contribution of the application to religious education and training, the contribution of teachers to professional and personal development, the effect of teachers' belonging to the school, the effectiveness, and improvement of the application. Permission has been obtained from the Ankara Provincial Directorate of National Education and participants via consent form for the interview. The interviews were recorded in audio, 169.58-minute audio recording transcripts, and 52-page interview data obtained. The qualitative data of this research has been analyzed with both descriptive and content analysis by NVivo 10 program. In order to ensure validity and reliability in the study, credibility, transferability, consistency, and verifiability strategies have been used.When the data of the research were analyzed, the opinions of DKAB teachers about the subject-based classrooms application categorized under four themes; positive and negative opinions/problems about the practice, the contribution of the practice to religious education and teaching and practical suggestions. Under the theme of positive opinions on the application, positive aspects for the teacher, student, parents, and comparison with the classroom system were formed. According to the participants, the practice of DKAB classroom is a “facilitation” element for the teacher because of the factors such as the teacher not moving around the classroom, not carrying material, having freedom to organize the classroom, and material regularity. In this application, since the teacher has a class of his own, the teacher adopts and owns the classroom and the sense of belonging to school increases. This sense of belonging is felt so strongly that there is even a participant who plans to retire if the application is abandoned. The positive aspects of the application for the students are that; a classroom is a motivating tool, the boards and walls can be used, and the student has the opportunity to exhibit their activities. When compared with the classroom system, it was stated that as a result of the branch classroom practice, the school equipment could be better protected, class order and cleaning easier, and the success of the students would be increased.The negative views of the participants on the application were grouped under the categories of students, teachers, and parents. Not having a fixed class of their own, continually relocating and losing time in recess, staying in the classroom door when the classroom is locked, not adopting the classroom and lack of lockers constitute the negative aspects of the application for the students. The overuse of classrooms, less use of teachers' rooms, lack of communication between colleagues are the negative aspects of the application for the teachers. It was stated by (75%) most of the participants that branch classroom application contributed to the continual learning of students in religious education and teaching. To make the application more functional, DKAB teachers have proposed arrangements such as improving physical infrastructure, enriching teaching materials, and arranging school syllabus and keeping classrooms open. As a result, teachers find the application more positive than the classroom system. While it is a motivation tool for primary school students, it causes some problems for secondary and high school students, such as carrying bags, staying at the door, and wasting time. In this sense, the application is recommended to be applied in primary schools that have a sufficient number of classrooms. Also, the system should be applied in secondary and high schools where the number of students is low, or the number of classrooms is sufficiently provided that the problems experienced by the students are eliminated. School administrations and teachers need to take necessary measures to make the application effective and efficient. (shrink)

This article assesses how autonomy and machine learning impact the existential risk of nuclear war. It situates the problem of cyber security, which proceeds by stealth, within the larger context of nuclear deterrence, which is effective when it functions with transparency and credibility. Cyber vulnerabilities poses new weaknesses to the strategic stability provided by nuclear deterrence. This article offers best practices for the use of computer and information technologies integrated into nuclear weapons systems. Focusing on nuclear command and control, avoiding (...) autonomy and machine learning is recommended as one means to reduce the existential risk of unintended nuclear conflict. (shrink)

Professional Ethics Code in Accounting constitutes a set of principles and values of everyday conduct of people whose work is connected with accounting. Accounting is an information system of enterprises, so it forms the basis of reliable management. Therefore information generated by accounting must be credible. Otherwise, the managing process would be based on false reports, which in consequence could lead to a fall of an economic subject, or it would at least cause the loss of clients, cooperators and associates` (...) trust. Accounting Association in Poland as an organization with more than one hundred years` tradition for people connected with enterprise accounting out of concern for reliability and fairness of carried out and registered economic transactions, recommended the first in Poland ethics code directed to accountants as a professional group. Professional Ethics Code in Accounting is a set of norms which aim is to help people connected with accounting solve ethical dilemmas appearing in their job. It can be said that this code is, so to speak, an answer to unfair accusations formulated against accountants being (in some people`s opinion) a group of “criminals in white collars”. It is obvious that there are different people with different characters in each society. Pathologies were and will be present everywhere. Accountants` environment is not free of them, which is proved by accounting scandals in Poland and abroad (for example the case of Enron company in the United States). In order to avoid generalizing, it must be underlined that among the ones connected with accounting, there are a lot of honest people caring about the company`s good name. (shrink)

Credibility is particularly important in organic food systems because there are only marginal visual and sensorial differences between organic and conventionally produced products, requiring consumers to trust in producers’ quality claims. In this article I explore what challenges the credibility of organic food systems and I explore how credibility of organic food systems can be maintained, using the Danish organic food system as a case study. The question is increasingly relevant as the sale of organic food is growing in Denmark (...) as well as globally, and consumers’ expectations of organics continuously evolve. The inquiry is threefold, first I outline a conceptual framework for understanding trust and credibility in the food system, secondly I explore the developments in Danish organic food systems and thirdly discuss the challenges and opportunities for maintaining trust in the Danish organic food system. In the analysis I indicate eight key challenges: unrealistic expectations, blind trust and little motivation for extending their knowledge, consumers assess the overall credibility of organic products, ambitious ethical principles, new consumer groups introduce new expectations, frozen requirements in a changing world, growing imports and labelling and multiple versions of organics and the diversity is growing, as well as four aspects which may maintain the credibility of organics if implemented: coordinate expectations, communicate requested information, institutional reform and open communication of pros and cons of organic production. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:The Materialities of CommunicationEric DeanThe Materialities of Communication, edited by Hans Ulrich Gumbrecht and K. Ludwig Pfeiffer; xvi & 447pp. Stanford: Stanford University Press, 1994, $52.50 cloth, $17.95 paper.In closing this collection, Hans Ulrich Gumbrecht outlines the common purpose which makes it more than a random assortment. There has been, as he characterizes it, a theoretical shift in the humanities “from interpretation as identification of given meaning-structures to (...) the reconstruction of those processes through which structures of articulated meaning can at all emerge” (p. 398). He motivates this shift by suggesting that theorists should no longer pursue final and therefore unchanging explanations, but rather should create provocations for changes in perspective. Focusing on the processes through which meaning can emerge—the materialities of communication rather than the insubstantialities of interpretation—stimulates the constant change, the perpetual “renegotiation of self-reference,” which Gumbrecht recommends as the rightful role of theory.The breadth of these ambitions is reflected in the scope of the collection. There are twenty-five essays from twenty-six writers, predominately German, including two from each editor—a context piece and a separate contribution. The range of subjects is enormous. There is one essay on ancient Egyptian signs and another on how the immune system creates the self it both recognizes and preserves. One touches on Nietzsche’s abortive attempt to save his eyesight by using an early version of the typewriter, another interprets the fall of the Roman Empire as the corruption of the postal system (Egyptian in origin) used to deliver the command, imperium, which constituted the Empire in the first place. The scholarly contexts range widely as well—from sociological communication theory to media history to biology to art criticism.There are some delightful curiosities the writers bring to light. For example, at one point in “The Early History of German Television,” the authors, M. Elsner, T. Muller, and P. Spangenberg are describing how fantasies about the new technology outran practicalities. They cite an illustration from a 1928 German magazine in which a man is shown lying in bed, “with a fragmented dashboard in front of him—he seems to steer some technical apparatus and, at the same time, to look at a mountain panorama, which is seen from a bird’s eye view on a kind of projection screen. The caption deciphers the illustration for us: ‘Marvels that we might still experience: viewing the world from bed through [End Page 395] television. The apparatus above the bed serves to operate, by remote control, an airplane that carries the filming apparatus...’” (p. 117).In other places too the writing evades the risk of self-absorption implicit, for all its purportedly exterior focus, in Gumbrecht’s theoretical project. Vivian Sobchack, for instance, closes an interesting meditation on film and electronic technologies this way: “Within the context of this material and technological crisis of the flesh, one can only hope that the hysteria and hyperbole surrounding it is strategic—and that through it the lived-body has, in fact, managed to reclaim its existence and against our attention, to forcefully argue for simulation. For there are other subjects of electronic culture out there who prefer the simulated body and a virtual world...” (pp. 105–6).By the end, though, I found myself wondering whether it was really possible to put this diversity together as a whole, whether the purportedly redeeming concreteness of a program focused on The Materialities of Communication wasn’t at odds with the idea that there is much in common among all these different materials. Coeditor K. Ludwig Pfeiffer, may have inadvertently provided the epigraph for the book. “Iago incites Othello to the greatest nonsense,” he writes, “because Othello no longer differentiates between manipulated sensual certainty and credibility” (p. 59). I wonder about a theoretical perspective that calls Othello’s torment his “nonsense,” and I wonder whether the advocates of such a perspective might not be manipulating sensual certainty to perhaps equally bleak, though in this case unintended, ends.Eric DeanChicago, IllinoisCopyright © 1995 The Johns Hopkins University Press... (shrink)