Friend Recommendation Feedback Algorithm Combining Cognition and Interest
YIN Yunfei1,2, SUN Jingqin1, HUANG Faliang3, BAI Xiangyu1
1. College of Computer Science, Chongqing University, Chong-qing 400044 2. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing 400044 3. School of Computer and Information Engineering, Nanning Normal University, Nanning 530001
Abstract In the existing friend recommendation algorithm, important information is lost in the portrayal of the friend relationship. Inspired by the user's cognitive behavior of the item, a friend recommendation feedback algorithm based on cognition and interest is proposed in this paper. Hybrid similarity is utilized to conduct online friend relationship research and explore friendship issues in online social networks. Aiming at the open loop problem of the friend recommendation process, a positive and negative feedback optimization adjustment strategy based on historical recommendation information is proposed. The user similarity correction formula is employed for friend feedback dynamic recommendation, and it is proved that friend recommendation is a complex process of gradual correction. The psychological and cognitive problems portrayed by friend relationships in online social networks and the dynamic changes of recommendations are presented. The experiments show that the proposed algorithm improves the recommendation quality and realizes the dynamic adjustment of the user similarity matrix and it is superior in accuracy, recall, robustness and scalability.
Corresponding Authors:
YIN Yunfei, Ph.D., associate professor. His research interests include artificial intelligence.
About author:: SUN Jingxin, master student. His research interests include recommendation system. HUANG Faliang, Ph.D., associated professor. His research interests include data mining and social media processing.BAI Xiangyu, undergraduate.
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2021, Vol. 34 
