Boosting Response Aware Model-Based Collaborative Filtering

Boosting Response Aware Model-Based Collaborative Filtering

Abstract— Boosting Response Aware Model-Based Collaborative Filtering. Recommender systems are promising for providing personalized favorite services. Collaborative filtering < Final Year Projects 2016 > technologies, making prediction of users’ preference based on users’ previous behaviors, have become one of the most successful techniques to build modern recommender systems. Several challenging issues occur in previously proposed CF methods. 1) most CF methods ignore users’ response patterns and may yield biased parameter estimation and suboptimal performance; 2) some CF methods adopt heuristic weight settings, which lacks a systematical implementation; and 3) the multinomial mixture models may weaken the computational ability of matrix factorization for generating the data matrix, thus increasing the computational cost of training.