A Hybrid User and Item-Based Collaborative Filtering with Smoothing on Sparse Data

Abstract

Collaborative filtering, the most successful recommender system technology to date, helps people make choices based on the opinions of other people. Existing collaborative filtering methods, mainly user-based and item-based methods, predict new ratings by aggregating rating information from either similar users or items. However, a large amount of ratings of similar items or similar users may be unavailable because of the sparse characteristic inherent to the rating data. For this reason, we present a Hybrid Predictive Algorithm with Smoothing (HSPA). HSPA uses item-based methods to provide the basis for data smoothing and builds the predictive model based on both users' aspects and items' aspects in order to ensure robust to data sparsity and predictive accuracy. Moreover, HSPA utilizes the user clusters to achieve high scalability. Experimental results from real datasets show that HSPA effectively contributes to the improvement of prediction on sparse data