Abstract: As a key solution to the problem of information overload,the recommender system can filter a large amount of information according to a user’s preference and provide personalized recommendations for users.This paper explored the area of personalized recommendation based on implicit feedback and proposed a recommendation model,namely implicit feedback recommendation model fusing context-aware and social network process(IFCSP),which is a novel context-aware recommender system incorporating processed social network information.This model handles contextual information by applying a decision tree algorithm to classify the original user-item-context selections so that the selections with similar contexts are grouped.Then implicit feedback recommendation model (IFRM) was employed to predict the preference of a user for a non-selected item using the partitioned matrix.In order to incorporate social network information,a regularization term was introduced to the IFRM objective function to infer a user’s preference for an item by learning opinions from his/her friends who are expected to share similar tastes.The study provides comparative experimental results based on the typical Douban and MovieLens-1M data sets.Finally,the results show that the proposed approach outperforms state-of-the-art recommendation algorithms in terms of mean average precision (MAP) and mean percentage ranking (MPR).

Key words: Recommender system,Implicit feedback,Context-awareness recommendation,Social recommendation,IFRM