A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
Diego Sánchez-Moreno, Vivian F. López Batista, M. Dolores Muñoz Vicente, Ana B. Gil González & María N. Moreno-García
Complexity 2020:1-13 (2020)
AbstractIn recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field involves dealing with many problems, some of which are generic and widely studied in the literature while others are specific to this application domain and are therefore less well-known. This work is focused on two important issues that have not received much attention: managing gray-sheep users and obtaining implicit ratings. The first one is usually addressed by resorting to content information that is often difficult to obtain. The other drawback is related to the sparsity problem that arises when there are obstacles to gather explicit ratings. In this work, the referred shortcomings are addressed by means of a recommendation approach based on the users’ streaming sessions. The method is aimed at managing the well-known power-law probability distribution representing the listening behavior of users. This proposal improves the recommendation reliability of collaborative filtering methods while reducing the complexity of the procedures used so far to deal with the gray-sheep problem.
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References found in this work
Neural Personalized Ranking Via Poisson Factor Model for Item Recommendation.Yonghong Yu, Li Zhang, Can Wang, Rong Gao, Weibin Zhao & Jing Jiang - 2019 - Complexity 2019:1-16.
Similarity-Based Summarization of Music Files for Support Vector Machines.Jan Jakubik & Halina Kwaśnicka - 2018 - Complexity 2018:1-10.
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