基于加权三部图的协同过滤推荐算法

doi:10.16451/j.cnki.issn1003-6059.202107008

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (700 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The over-concentration of recommendation results of user-based collaborative filtering algorithm on popular items causes the lack of diversity, novelty and coverage. Aiming at this problem, a collaborative filtering recommendation algorithm based on weighted tripartite network is proposed. Based on sparse analysis data and little additional information, tags are introduced to reflect user interests and item attributes simultaneously. Ternary relationships among users, items and tags are utilized to construct a tripartite network.The user preference is obtained by projecting the tripartite network to the one-mode network, and a tripartite network model weighted by user preference is constructed. According to the heat spreading method, resources are redistributed on the weighted tripartite network to find more similarity relationships. The standard framework of collaborative filtering is applied for prediction and recommendation. Experiments on real datasets show that the proposed method mines long-tail items better and realizes personalized recommendations.

Key words： Collaborative Filtering Weighted Tripartite Network User Preference Heat Spreading

Received: 25 September 2020

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.61976109), Doctoral Startup Project of Natural Science Foundation of Liaoning Province(No. 2020-BS-184), Dalian Science and Technology Innovation Fund (No. 2018J12GX047),Dalian High Level Talents Innovation Support Project (No. 2020RQ49), Special Fund of Dalian Key Laboratory

Corresponding Authors: ZHANG Zhipeng, Ph.D., lecturer. His research interests include data mining and recommender system.

About author:: REN Yonggong, Ph.D., professor. His research interests include artificial intelligence and data mining.WANG Ningjing, master student. Her research interests include artificial intelligence and data mining.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	REN Yonggong
	WANG Ningjing
	ZHANG Zhipeng

Cite this article:

REN Yonggong,WANG Ningjing,ZHANG Zhipeng. Collaborative Filtering Recommendation Algorithm Based on Weighted Tripartite Network[J]. , 2021, 34(7): 666-676.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202107008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I7/666

[1] CHEN R, HUA Q Y, CHANG Y S, et al. A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks. IEEE Access, 2018, 6: 64301-64320.
[2] ADOMAVICIUS G, TUZHILIN A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[3] JAFFALI S, JAMOUSSI S, SMAILI K, et al. Like-Tasted User Groups to Predict Ratings in Recommender Systems. Social Network Analysis and Mining, 2020, 10(1): 42-52.
[4] LIU X C, SU X, MA J M, et al. Information Filtering Based on Eliminating Redundant Diffusion and Compensating Balance. Inter-national Journal of Modern Physics B, 2019, 33(13). DOI: 10.1142/S0217979219501297.
[5] 孟桓羽,刘真,王芳,等.基于图和改进K近邻模型的高效协同过滤推荐算法.计算机研究与发展, 2017, 54(7): 1426-1438.
(MENG H Y, LIU Z, WANG F, et al. An Efficient Collaborative Filtering Algorithm Based on Graph Model and Improved KNN. Journal of Computer Research and Development, 2017, 54(7): 1426-1438).
[6] MOLAEI S, ZARE H, VEISI H. Deep Learning Approach on Information Diffusion in Heterogeneous Networks. Knowledge Based Systems, 2020, 189. DOI: 10.1016/j.knosys.2019.105153.
[7] SONG A B, LIU Y Y, WU Z A, et al. A Local Random Walk Model for Complex Networks Based on Discriminative Feature Combinations. Expert Systems with Applications, 2019, 118: 329-339.
[8] ZARE H, POUR M A N, MORADI P. Enhanced Recommender System Using Predictive Network Approach. Physica A(Statistical Mechanics and Its Applications), 2019, 520(8): 322-337.
[9] ZHANG L, WEI Q S, ZHANG L, et al. Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems. Applied Science, 2020, 10(4): 1257-1272.
[10] MA T M, WANG X, ZHOU F C, et al. Research on Diversity and Accuracy of the Recommendation System Based on Multi-objective Optimization. Neural Computing and Applications, 2020, 33(3). DOI: 10.1007/S00521-020-05438-W.
[11] YI J, ZHONG M S, CHEN Y F, et al. A Hybrid Collaborative Filtering Recommendation Algorithm Based on User Attributes and Matrix Completion//Proc of the 2nd International Conference on Communication, Network and Artificial Intelligence. Bristol, UK: IOP, 2019: 388-395.
[12] ZHAO Z, HONG L C, WEI L, et al. Recommending What Video to Watch Next: A Multitask Ranking System//Proc of the 13th ACM Conference on Recommender Systems. New York, USA: ACM, 2019: 43-51.
[13] LIN X, CHEN H J, PEI C H, et al. A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation//Proc of the 13th ACM Conference on Recommendation Systems. New York, USA: ACM, 2019: 20-28.
[14] HAMEDANI E M, KAEDI M. Recommending the Long Tail Items through Personalized Diversification. Knowledge Based Systems, 2019, 164(2): 348-357.
[15] GOGNA A, MAJUMDAR A. DiABIO: Optimization Based Design for Improving Diversity in Recommender System. Information Sciences, 2017, 378: 59-74.
[16] HU J Y, GAO Z W, PAN W S. Multiangle Social Network Reco-mmendation Algorithms and Similarity Network Evaluation[J/OL]. [2020-08-23]. https://www.emis.de/journals/HOA/JAM/Volume2013/248084.pdf.
[17] CANTADOR I, BRUSILOVSKY P, KUFLIK T. Second Workshop on Information Heterogeneity and Fusion in Recommender Systems
//Proc of the 5th ACM Conference on Recommender Systems. New York, USA: ACM, 2011: 387-388.
[18] GAN M X, JIANG R. Constructing a User Similarity Network to Remove Adverse Influence of Popular Objects for Personalized Re-commendation. Expert Systems with Applications, 2013, 40(10): 4044-4053.
[19] 谭昶,刘淇,吴乐,等.推荐系统中典型用户群组的发现和应用.模式识别与人工智能, 2015, 28(5): 462-471.
(TAN C, LIU Q, WU L, et al. Finding and Applying Typical User Group in Recommender Systems. Pattern Recognition and Artificial Intelligence, 2015, 28(5): 462-471.)
[20] REN X L, LÜ L Y, LIU R R, et al. Avoiding Congestion in Re-commender Systems. New Journal of Physics, 2014, 16(6): 1367-1385.
[21] JALILI M, AHMADIAN S, IZADI M, et al. Evaluating Collaborative Filtering Recommender Algorithms: A Survey. IEEE Access, 2018, 6: 74003-74024.