Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

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.

Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

“…With the rapid rise of Internet technology and electronic information technology, it is becoming more and more important and valuable to find the required information quickly and accurately in such a huge amount of information [14]. e resultant recommendation engine has evolved into a link between users' wants and material, allowing users to not only locate possible content they are interested in, but also better present unpopular content and discover new people [15]. e use of recommendation system technology to music is a significant one.…”

A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern

“…Existing metric learning-based models have achieved satisfactory results, but these models still face the challenge of data sparsity. To alleviate this problem, they have introduced social information and achieved a certain degree of success [7]. However, all of these models overlook an important issue: social information is often as sparse as rating data, and most users' social information is still very sparse.…”

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

“…It is worth mentioning, that the proposed approach (i) does not need any kind of supplementary information, apart from users' ratings on items, and hence can be applied in any CF dataset and (ii) can be fused with other CF approaches, aiming to enhance rating prediction accuracy or efficiency, either using supplementary sources of information, such as users' relations in social networks and detailed characteristics of items [35][36][37] or not [38][39][40].…”