The effect of rating variance on personalized recommendation

ICCSE 2010 - 5th International Conference on Computer Science and Education, Final Program and Book of Abstracts

Volumn , Issue , 2010, Pages 366-370

  Zhu, Yu Xiao ^a   Zeng, Wei ^a   Zhang, Qian Ming ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Accuracy; Collaborative filtering; Diversity; Rating variance; Recommender systems

Indexed keywords

ACCURACY; ACCURACY INDEX; COLLABORATIVE FILTERING; DIVERSITY; DIVERSITY INDEX; MEAN ABSOLUTE ERROR; PERSONALIZED RECOMMENDATION; RECOMMENDATION ALGORITHMS; RECOMMENDER SYSTEMS;

ALGORITHMS; COMPUTER SCIENCE; EDUCATION COMPUTING; SIGNAL FILTERING AND PREDICTION;

ENGINEERING EDUCATION;

EID: 78649411687     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCSE.2010.5593610     Document Type: Conference Paper

References (14)

1. G. Adomavicius, S. Kamireddy, and Y. Kwon, Towards More Confident Recommendations:Improving Recommender Systems Using Filtering Approach Based on Rating Variance, the 17th Workshop on Information Technology and Systems, No. 6. (2007), pp.634C749.
2. G. Adomavicius and A. Tuzhilin, Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions", Knowledge and Data Engineering, IEEE Transactions, Vol. 17, No. 6. (2005), pp.634-749.
3. J. L. Herlocker, J. A. Konstan, A. Borchers and John Riedl,An Algorithmic Framework for Performing Collaborative Filtering", 22nd ACM SIGIR Conference on Information Retrieval, Vol. 35 . (1999), pp. 230-237.
4. J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon and J. Riedl, GroupLens: Applying Collaborative Filtering to Usenet News", Communications of the ACM, Vol. 40. (1997), pp.77-87
5. G. Linden, B. Smith and J. York, Amazon.com Recommendations: Item-to-item Collaborative Filtering",IEEE Internet Computing, 7 (2003), pp. 76-80.
6. J.L. Herlocker, J.A. Konstan, L.G. Terveen, J. Riedl, Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, 22(1):5-53, 2004.
7. Gediminas.Adomavicius, YoungOk. Kwon, Overcoming Accuracy-Diversity Tradeoff in Recommender Systems:A Variance-Based Approach",Proceedings of the 18th Workshop on Information Technology and Systems,2008.
8. Maslov S and Zhang Y C, Extracting Hidden Information from Knowledge Networks " Phys. Rev. Lett. 2001
9. Jie Ren, Tao Zhou and Yi-Cheng Zhang, "Information filtering via self-consistent refinement", Europhysics Letters, Issue Volume 82, Number 5, 2008.
10. Ken Goldberg, Theresa Roeder, Dhruv Gupta and Chris Perkins, "A Constant Time Collaborative Filtering Algorithm", Information Retrieval, 2004.
11. Yi-Cheng Zhang and Marcel Blattner, "Heat Conduction Process on Community Networks as a Recommendation Model", Phys. Rev. Lett. 2007.
12. Shang M.-S., Zhang Z.-K., Zhou T., Zhang Y.-C.. Collaborative filtering with diffusion-based similarity on tripartite graphs. Physica A,2010,389(6):1259-1264
13. Shang M.-S., Jin C.-H., Zhou T., Zhang Y.-C.. Collaborative filtering based on multi-channel diffusion. Physica A, 2009,388(23):4867-4871
14. Shang M.-S., L L.-Y., Zeng W., Zhou T., Zhang Y.-C.. Relevance is more significant than correlation: Information filtering on sparse data. EPL(Europhysics Letters),2009,88(12):68008.