Network-based models for social recommender systems
This work addresses the need for effective recommender systems to filter online products for users, though it appears incremental as it builds on existing group-based assumptions.
The authors tackled the problem of personalized recommendation by developing network-based models that assume preferences are determined by group memberships of users and items, resulting in performance that outperforms leading approaches.
With the overwhelming online products available in recent years, there is an increasing need to filter and deliver relevant personalized advice for users. Recommender systems solve this problem by modeling and predicting individual preferences for a great variety of items such as movies, books or research articles. In this chapter, we explore rigorous network-based models that outperform leading approaches for recommendation. The network models we consider are based on the explicit assumption that there are groups of individuals and of items, and that the preferences of an individual for an item are determined only by their group memberships. The accurate prediction of individual user preferences over items can be accomplished by different methodologies, such as Monte Carlo sampling or Expectation-Maximization methods, the latter resulting in a scalable algorithm which is suitable for large datasets.