基于信念网络的协同过滤图模型的推荐算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201602009

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Abstract Information retrieval model has been applied to the collaborative filtering algorithm now. The belief network model in information retrieval is used to describe user-based collaborative filtering and item-based collaborative filtering uniformly, and a recommendation algorithm of collaborative filtering graph model based on belief network is put forward. Due to the property that belief network is convenient to combine the information of additional sources, the expert information is added to the collaborative filtering model to provide decision support for the users, and consequently the data sparse problem of the recommendation system is solved. Experimental results show that the proposed algorithm improves the recommendation accuracy.

Key words： Information Retrieval Personalized Recommendation Expert Information Belief Network Model

Received: 29 May 2015

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	ZHU Kunlei
	HUANG Jiajin

Cite this article:

ZHU Kunlei,HUANG Jiajin. Recommendation Algorithm of Collaborative Filtering Graph Model Based on Belief Network[J]. , 2016, 29(2): 171-176.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201602009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I2/171

[1] BREESE J S, HECKERMAN D, KADIE C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering // Proc of the 14th Conference on Uncertainty in Artificial Intelligence. Madison, USA, 1998: 43-52.
[2] YANG Y M. Expert Network: Effective and Efficient Learning from Human Decisions in Text Categorization and Retrieval // Proc of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 1994: 13-22.
[3] SIVIC J, ZISSERMAN A. Video Google: A Text Retrieval Approach to Object Matching in Videos // Proc of the 9th IEEE International Conference on Computer Vision. Nice, France, 2003: 1470-1477.
[4] SALTON G. The SMART Retrieval System-Experiments in Automa-tic Document Processing. Information Storage and Retrieval, 1973, 9(3): 193-199.
[5] SALTON G, LEEK M E. Computer Evaluation of Indexing and Text Processing. Journal of the ACM, 1968, 15(1): 8-36.
[6] JUDEA P. Probabilistic Reasoning in Intelligent System: Networks of Plausible Inference. New York: USA: Morgan Kaufmann Publishers, 1988.
[7] RIBEIRO B A, MUTZ R. A Belief Network Model for IR // Proc of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Zurich, Switzerland, 1996: 253-260.
[8] CHAKRABARTI S, DOM B, RAGHAVAN P, et al. Automatic Resource List Compilation by Analyzing Hyperlink Structure and Associated Text // Proc of the 7th International World Wide Web Conference. Brisbane, Australia, 1998: 65-74.
[9] COSTA A L, RODA F. Recommender Systems by Means of Information Retrieval // Proc of the International Conference on Web Intelligence, Mining and Semantics. Sogndal, Norway, 2011: 126-136.
[10] WANG J, DE VRIES A P, REINDERS M J T. A User-Item Relevance Model for Log-Based Collaborative Filtering[EB/OL]. [2015-04-25]. http://homepages.cwi.nl/~arjen/pub/ecir06.pdf.
[11] WANG J, ROBERTSON A P, DE VRIES M J T. Probabilistic Relevance Ranking for Collaborative Filtering. Information Retrieval, 2008, 11(6): 477-497.
[12] BELLOGIN A, WANG J, CASTELLS P. Ridging Memory-Based Collaborative Filtering and Text Retrieval. Information Retrieval, 2013, 16(6): 697-724.
[13] KARYPIS G. Item-Based Top-n Recommendation Algorithms. ACM Trans on Information and Systems, 2004, 22(1): 143-177.
[14] KLEINBERG J M. Authoritative Sources in a Hyperlinked Environment. Journal of the ACM, 1999, 46(5): 604-632.
[15] AGICHTEIN E, BRILL E, DUMAIS S, et al. Learning User Interaction Models for Predicting Web Search Result Preferences // Proc of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, USA, 2006: 3-10.
[16] SALTON G, WONG A, YANG C S. A Vector Space Model for Automatic Indexing. Communications of the ACM, 1975, 18(11):
613-620.
[17] VALCARCE D, PARAPAR J, BARREIRO . A Study of Smoothing Methods for Relevance-Based Language Modelling of Recommender Systems // Proc of the 37th European Conference on Information Retrieval Research. Vienna, Austria, 2015: 346-351.
[18] ADOMAVICIUS G, TUZHILIN A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[19] DESROSIERS C, KARYPIS G. A Comprehensive Survey of Neighborhood-Based Recommendation Methods[EB/OL]. [2015-04-23]. http://www.ics.uci.edu/~welling/teaching/CS77 Bwinter12/handbook/NbrRSsurvey2011.pdf.
[20] HENRION M. Practical Issues in Constructing a Bayes′ Belief Network // Proc of the 3rd Conference on Uncertainty in Artificial Intelligence. Seattle, USA, 2013: 132-142.
[21] LEE J W, KIM H J. Exploiting Taxonomic Knowledge for Personalized Search: A Bayesian Belief Network-Based Approach. Information Science and Engineering, 2011, 27(4): 1413-1433.