A Collaborative Filtering Recommendation Algorithm Combining Probabilistic Relational Models and User Grade

Collaborative filtering is one of successful technologies for building recommender systems. Unfortunately, it suffers from sparsity and scalability problems. To address these problems, a collaborative filtering recommendation algorithm combining probabilistic relational models and user grade （PRM-UG-CF） is presented. PRM-UG-CF has primary two parts. First, a user grade function is defined, and user grade based collaborative filtering method is used, which can find neighbors for the target user only in his near grade, and the number of candidate neighbors can be controlled by a parameter, so recommendation efficiency is increased and it solves the scalability problem. Second, in order to use various kinds of information for recommendation, user grade based collaborative filtering method is combined with probabilistic relational models （PRM）, thus it can integrate user information, item information and user-item rating data, and use adaptive strategies for different grade users, so recommendation quality is improved and it solves the sparsity problem. The experimental results on MovieLens data set show that the algorithm PRM-UG-CF has higher recommendation quality than a pure PRM-based or a pure collaborative filtering approach, and it also has much higher recommendation efficiency than a pure collaborative filtering approach.