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References

1. Bigdeli, E., and Bahmani, Z. Comparing accuracy of cosine-based similarity and correlation-based similarity algorithms in tourism recommender systems. 4th IEEE International Conference on Management of Innovation and Technology, ICMIT 2008.
2. PAZZANI, M. J. , A Framework for Collaboratvie, Content-Based and Demographic Filtering, Artificial Intelligence Review 13: 394-408, 1999
3. N. Good, J. Schafer, J. Konstan, A. Borchers, B. Sarwar, J. Herlocker, and J. Riedl. Combining collaborative filtering with personal agents for better recommendalions. In Proceedings of the National Conference on Artificial lntelligence, pages 439.446. 1999
4. P. Resnick, N. lacovou, M. Suchak, P. Bergstrom, and J. Riedl. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pages 175.186.1994.
5. B. Sarwar, G. Karypis, J. Konstan, and J. ReidI. ltembased collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285.295.2001.
6. B. Sarwar, J. Konstan, A. Borchers, J. Herlocker, B. Miller, and J. Riedl. Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. In Proceedings of the 1998 ACM conference on Computer supported cooperative work, pages 345 .354. 1998.
7. J. Wang, A. A. P. de Vries, and M. M. J. T. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 501.508, 2006 .
8. G. Chen, F. Wang, and C. Zhang. Collaborative filtering using orthogonal nonnegative matrix tri-factorization. Infonnation Processing & Management, 45(3):368.379, 2009.
9. M. Gori, A. Pucci, and V. Roma. Itemrank: A randomwalk based scoring algorithm for recommender engines. In 20th lntemational Joint Conference on Artificial Intelligence(IJCAI07),
10. Matrix Factorization Techniques for Recommender Systems YehudaKoren, Robert Bell, Chris Volinsky In IEEE Computer, Vol 42, No. 8.(07 August 2009), pp. 30-37.
11. Yunhong Zhou, Dennis Wilkinson, Robert Schreiber and Rong Pan. Large-Scale Parallel Collaborative Filtering for the Netflix Prize. Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management. Shanghai, China pp. 337- 348,2008.
12. H.-F. Yu, C.-J. Hsieh, S. Si, I. S. Dhillon, Scalable Coordinate Descent Approaches to Parall el Matrix Factorization for Recommender Systems. IEEE International Conference on Data Mining(lCDM), December 2012.
13. Y. Koren. Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model. In ACM KDD 2008.
14. Paolo Cremonesi, Yehuda Koren, Roberto Turrin: Performance of recommender algorithms on top-n recommendation tasks. Proceedings of the fourth ACM conference on Recommender systems, RecSys 2010: 39-46
15. Shi Y. and Karatzoglou A. and Baltrunas L. and Larson M. and Oliver N and Han, CLiMF: Learning to Maximize Reciprocal Rank with Collaborat ive Less-is-more Filtering, Proceedings of tbe Sixth ACM Conference on Recommender Systems, RecSys 2012: I39- I46
16. Markus W. and Alexandros K. and Quoc V. L. and Alex S., CofiRank - Maximum Margin Matrix Factorization for Collaborative Ranking, Advances in Neural Information Process ing Systems(NlPS) 2007. pp. 1593- 1600
17. R. Gemulla, E. Nijkamp, P. J. Haas, and Y. Sismanis. Large-scale matrix factorization with distributed stochastic gradient descent. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ' 11 .
18. Y. Zhuang, W.-S. Chin, Y.-C. Juan, and C.-J. Lin. A Fast Parallel SGD for Matrix Factorization in Shared Memory Systems. Proceedings of ACM Recommender Systems 2013.