Computer Science ›› 2016, Vol. 43 ›› Issue (6): 248-253.doi: 10.11896/j.issn.1002-137X.2016.06.049

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Implicit Feedback Personalized Recommendation Model Fusing Context-aware and Social Network Process

YU Chun-hua, LIU Xue-jun and LI Bin   

  • Online:2018-12-01 Published:2018-12-01

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

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