Research Journal of Applied Sciences

Year: 2016
Volume: 11
Issue: 10
Page No. 1102 - 1106

Improving Recommendation System Based on Homophily Principle and Demographic

Authors : Zainab Khairallah and Huda Naji Nawaf

References

Ahn, H.J., 2008. A new similarity measure for collaborative filtering to alleviate the new user Cold-starting problem. Inform. Sci., 178: 37-51.
CrossRef  |  

Beel, J., L.S. Nurnberger and M.A. Genzmehr, 2013. The Impact of Demographics (Age and Gender) and other User-Characteristics on Evaluating Recommender System. Springer, Berlin, Germany,.

Breese, J.S., D. Heckerman and C. Kadie, 1998. Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Jul 24-26, 1998, Madison, WI., pp: 43-52.

Cheng, Q., X. Wang, D. Yin, Y. Niu and X. Xiang et al., 2015. The new similarity measure based on user preference models for collaborative filtering. Proceedings of the IEEE International Conference on Information and Automation, August 8-10, 2015, IEEE, New York, USA., ISBN:978-1-4673-9104-7, pp: 577-582.

Cremonesi, P., R. Turrin, E. Lentini and M. Matteucci, 2008. An evaluation methodology for collaborative recommender systems. Proceedings of the International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution, November 17-19, 2008, IEEE, New York, USA., ISBN:978-0-7695-3406-0, pp: 224-231.

Gogna, A. and A. Majumdar, 2015. A comprehensive recommender system model: Improving accuracy for both warm and cold start users. IEEE. Access, 3: 2803-2813.
CrossRef  |  Direct Link  |  

Hofmann, T. and J. Puzicha, 1999. Latent class models for collaborative filtering. Proceedings of the 16th International Joint Conference in Artificial Intelligence, July 31-August 6, 1999, San Francisco, CA., USA., pp: 688-693.

Huang, X., Z. Qin and H.A. Chen, 2015. New user similarity measurement based on a local item space in collaborative filtering recommendation. J. Comput. Inf. Syst., 11: 3501-3508.

Ju, C. and C. Xu, 2013. A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm. Sci. World J., 2013: 1-9.
Direct Link  |  

Kaufman, L. and P.J. Rousseeuw, 1990. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley and Sons, New York, ISBN: 0471878766, Pages: 342.

Miyahara, K. and M.J. Pazzani, 2000. Collaborative Filtering with the simple Bayesian classifier. In: Pacific Rim International conference on artificial intelligence, Mizoguchi, R. and J. Slaney (Eds.). Springer, Berlin, Germany, ISBN: 978-3-540-44533-3, pp: 679-689.

Miyahara, K. and M.J. Pazzani, 2000. Improvement of collaborative filtering with the simple Bayesian classifier. Inf. Technol. R. D. Center Mitsubishi Electr. Coporation, 2000: 679-689.

Murty, M.N. and V.S. Devi, 2011. Pattern Recognition: An Algorithmic Approach. Springer, Berlin, Germany, pp: 93-97.

Pazzani, M.J., 1999. A framework for collaborative, content-based and demographic filtering. Artificial Intell. Rev., 13: 393-408.
Direct Link  |  

Pillay, N., A.P. Engelbrecht, A. Abraham, M.C.D. Plessis and V. Snasel et al., 2015. Advances in nature and biologically inspired computing. Proc. World Congress Nature Biol. Inspired Comput., 2015: 39-41.

Polatidis, N. and C.K. Georgiadis, 2016. A multi-level collaborative filtering method that improves recommendations. Expert Syst. Appl., 48: 100-110.
Direct Link  |  

Ricci, F., L. Rokach and B. Shapira, 2015. Recommender Systems Handbook. 2nd Edn., Springer, Berlin, Germany,.

Shamri, M.Y.H.A. and N.H.A. Ashwal, 2014. Fuzzy-weighted similarity measures for memory-based collaborative recommender systems. J. Intell. Learn. Syst. Appl., 6: 1-10.
CrossRef  |  Direct Link  |  

Sun, H.F., J.L. Chen, G. Yu, C.C. Liu and Y. Peng et al., 2012. JacUOD: A new similarity measurement for collaborative filtering. J. Comput. Sci. Technol., 27: 1252-1260.
CrossRef  |  Direct Link  |  

Treerattanapitak, K. and C. Jaruskulchai, 2012. Exponential fuzzy C-means for collaborative filtering. J. Comput. Sci. Technol., 27: 567-576.
CrossRef  |  Direct Link  |  

Wang, K. and Y. Tan, 2011. A New Collaborative Filtering Recommendation Approach Based on Naive Bayesian Method. In: International Conference in Swarm Intelligence, Ying T., S. Yuhui, Y. Chai and G. Wang (Eds.). Springer, Berlin, Germany, ISBN: 978-3-642-21524-7, pp: 218-227.

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