Author:
Tan Jingwen,Hu Youxin,Luo Jianqiu
Abstract
For traditional collaborative filter recommendation algorithm technology, this paper combines the collaborative filtering recommendation algorithm with the community division technology of social networks, use the Louvain community to divide algorithms, divide the recommendation users to a community of similar users, and use the collaborative filter algorithm based on the user similarity formula within the community to recommend. In order to verify the effectiveness and accuracy of the algorithm in this paper, based on the introduction of the Douban dataset and the evaluation criteria used, a variety of comparative experiments are carried out on the Douban dataset with a variety of recommendation algorithms to verify the effectiveness of the proposed algorithm
Publisher
Darcy & Roy Press Co. Ltd.
Reference13 articles.
1. Bedi P, Sharma C. Community detection in social networks [J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2016, 6(3): 115-35.
2. Zhang Q,Weng L. A systematic review of social recommendation[J].Computer Engineering and Application,2020,56(01):1-10.
3. Zhou W , Li J , Zhang M , et al. Incorporating Social Network and User's Preference in Matrix Factorization for Recommendation[J]. Arabian Journal for Science and Engineering, 2018, 43(12):8179-8193.
4. FE Walter, Battiston S , Schweitzer F . A model of a trust-based recommendation system on a social network[J]. Autonomous Agents and Multi-Agent Systems, 2008, 16(1):57-74.
5. Zhang X,Chen X,et al. Top-N recommendation algorithm integrating user trust and influence[J].Journal of Zhejiang University(Engineering Science),2020,54(02):311-319.)