Abstract
Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates.
Highlights
Social networks are currently the focus of intensive research, as they are a great source of information that can be used in multiple domains for multiple purposes
The implementation of methods that take advantage of social information is scarce in the music streaming services environment, because the mechanisms of social interaction are much more limited than in social networks such as Facebook, Twitter, etc
The growing use of music streaming services and the interest in their personalization is unquestionable nowadays. This is one of the main motives why the surge in intensive research in many areas on the exploitation of information from social networks has been extended to music recommender systems
Summary
Social networks are currently the focus of intensive research, as they are a great source of information that can be used in multiple domains for multiple purposes. The adoption of streaming music services as a common way of listening to music has allowed its use in this domain since most of these platforms are, in turn, equipped with some kind of social functionality, such as establishing friendship connections. Streaming systems collect user interactions, which allows implicit feedback from users to be used instead of explicit ratings as an expression of user preferences. This has promoted the development of recommender systems for these platforms. The implementation of methods that take advantage of social information is scarce in the music streaming services environment, because the mechanisms of social interaction are much more limited than in social networks such as Facebook, Twitter, etc
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