Collaborative filtering recommendation algorithm based on cloud model

Ruan Jian Xue Bao/Journal of Software

Volumn 18, Issue 10, 2007, Pages 2403-2411

  Zhang, Guang Wei ^a,d   Li, De Yi ^b   Li, Peng ^c   Kang, Jian Chu ^a   Chen, Gui Sheng ^b  

^a BEIHANG UNIVERSITY   (China)

^b INSTITUTE OF ELECTRONICS   (China)

^c HARBIN INSTITUTE OF TECHNOLOGY   (China)

^d SHANDONG JIANZHU UNIVERSITY   (China)

Author keywords

Cloud model; Collaborative filtering; Recommendation system; Similarity; Voting

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FUZZY SETS;

COLLABORATIVE FILTERING ALGORITHM; KNOWLEDGE LEVEL; LIKENESS COMPARING METHOD CLOUD MODEL (LICM); RECOMMENDATION SYSTEM; SIMILARITY; USER RATING DATA;

ELECTRONIC COMMERCE;

EID: 35948983013     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.1360/jos182403     Document Type: Article

References (22)

1. Zan H, Hsinchun C, Daniel Z. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. on Information Systems, 2004,22(1):116-142.
2. Thiesson B, Meek C, Chickering DM, Heckerman D. Learning mixture of DAG models1 microsoft research. Technical Report, MSR2TR297230, 1997.
3. Sarwar B, Karypis G, Konstan J, Riedl J. Analysis of recommendation algorithms for E-commerce. In: Proc. of the 2nd ACM Conf. on Electronic Commerce. New York: ACM Press, 2001. 158-167. http://www.research.ibm.com/iac/ec00/
4. Aggarwal CC, Wolf J, Wu KL, Yu PS. Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In: Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 1999. 201-212.
5. Goldberg D, Nichols D, Oki BM, Terry D. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 1992,35(12):61-70.
6. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. Grouplens: An open architecture for collaborative filtering of netnews. In: Proc. of the ACM CSCW'94 Conf. on Computer-Supported Cooperative Work. Chapel Hill: ACM, 1994. 175-186.
7. Shardanand U, Maes P. Social information filtering: Algorithms for automating 'Word of Mouth'. In: Proc. of the ACM CHI'95 Conf. on Human Factors in Computing Systems. New York: ACM Press/Addison-Wesley Publishing Co., 1995. 210-217.
8. Hill W, Stead L, Rosenstein M, Furnas G. Recommending and evaluating choices in a virtual community of use. In: Proc. of the CHI'95. New York: ACM Press/Addison-Wesley Publishing Co., 1995. 194-201.
9. Breese J, Hecherman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the 14th Conf. on Uncertainty in Artificial Intelligence (UAI'98). San Francisco: Morgan Kaufmann Publishers, 1998. 43-52.
10. Sarwar BM, Karypis G, Konstan JA, Riedl J. Application of dimensionality reduction in recommender system-A case study. In: Proc. of the ACM WebKDD 2000 Workshop. 2000. http://robotics.stanford.edu/-ronnyk/WEBKDD2000/
11. Zhao L, Hu NJ, Zhang SZ. Algorithm design for personalization recommendation systems. Journal of Computer Research and Development, 2002,39(8):986-991 (in Chinese with English abstract).
12. Aggarwal CC. On the effects of dimensionality reduction on high dimensional similarity search. In: Proc. of the ACM PODS Conf. Santa Barbara: ACM, 2001.
13. Deng AL, Zhu YY, Shi BL. A collaborative filtering recommendation algorithm based on item rating prediction. Journal of Software, 2003,14(9):1621-1628 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/14/1621.htm
14. Zhou JF, Tang X, Guo JF. An optimized collaborative filtering recommendation algorithm. Journal of Computer Research and Development, 2004,41(10):1842-1847 (in Chinese with English abstract).
15. Liu Q. Research on some key technologies of Chinese-English machine translation [Ph.D Thesis]. Beijing: Peking University, 2004 (in Chinese with English abstract).
16. Li DY. Artificial Intelligence with Uncertainty. Beijing: National Defense Industry Press, 2005. 171-177 (in Chinese).
17. Li DY, Liu CY. Study on the universality of the normal cloud model. Engineering Science, 2004,6(8):28-34 (in Chinese with English abstract).
18. Li DY, Liu CY, Du Y, Han X. Artificial intelligence with uncertainty. Journal of Software, 2004,15(11):1583-1594 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/15/1583.htm
19. Li DY. Uncertainty in knowledge representation. Engineering Science, 2000,2(10):73-79 (in Chinese with English abstract).
20. Zhang BQ. A collaborative filtering recommendation algorithm based on domain knowledge. Computer Engineering, 2005,31(21):7-9 (in Chinese with English abstract).
21. Sarwar B, Karypis G, Konstan J, Riedl J. Item-Based collaborative filtering recommendation algorithms. In: Proc. of the 10th Int'l World Wide Web Conf. Hong Kong: ACM Press, 2001. 285-295.
22. Zhang F, Chang HY. Employing BP neural networks to alleviate the sparsity issue in collaborative filtering recommendation algorithms. Journal of Computer Research and Development, 2006,43(4):667-672 (in Chinese with English abstract).