A Model for Collaborative Filtering Recommendation in E-Commerce Environment
Keywords:
Integration of real-time information, one class collaborative filtering, e-commerceAbstract
In modern business environment, product life cycle gets shorter and the customer’s buying preference changes over time. Time plays a more and more important role in collaborative filtering. However, there is a gap in one class collaborative filtering (OCCF). On the basis of collecting different real-time information, this paper proposes an optimization model for e-retailers. Through comparing different methods with different weights, results show that real-time dependent in OCCF performs better in improving the quality of recommendation. The model is effective in cross-selling e-commerce, personalized, targeted recommendation sales.
References
Goldberg D., D. Nichols, B. M. Oki, and Terry D., Using collaborative filtering to weave an information tapestry, Communications of ACM, 35 (12): 61-70,1992. http://dx.doi.org/10.1145/138859.138867
Li G. and Li L., Based on matrix decomposition algorithm for single-class collaborative filtering recommendation, Application Research of Computers, 29(5): 1662-1665, 2012.
Shani G., D. Heckerman, and R. I. Brafman, An MDP-based recommender system, Journal of Machine Learning Research, 6: 1265-1295,2005.
Banati H. and Mehta S., A Multi-Perspective Evaluation of ma and ga for collaborative Filtering Recommender System, International J of Computer Science & Information Technology 2(5): 103-22, 2010. http://dx.doi.org/10.5121/ijcsit.2010.2508
Yang S. and Xue W., Classification based on a single collaborative filtering recommendation algorithm, Computer Engineering, 19(37): 59-61,2011.
Pan R. and M. Scholz., Mind the gaps: Weighting the unknown in largescale one-class collaborative, In Proceedings of the 15 th ACM SIGKDD international conference on Knowledge discovery and data mining, 667-676, 2009. http://dx.doi.org/10.1145/1557019.1557094
Sindhwani V., S.S. Bucak, J. Hu, and A. Mojsilovic, A family of nonnegative matrix factorizations for one-class collaborative filtering problems, In Proc. of 3rd ACM RecSys Workshop on Recommendation-based Industrial Applications, 2009.
Koren Y., Collaborative filtering with temporal dynamics, In Proc. of 15 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 447-456, 2009. http://dx.doi.org/10.1145/1557019.1557072
Lu Z., D. Agarwal, and I. S. Dhillon, A spatio-temporal approach to collaborative filtering, In Proc. of 3rd ACM RecSys Workshop on Recommendation-based Industrial Applications.13- 20, 2009.
Xiang L. and Yang Q., Time-dependent models in collaborative filtering based recommender system, In Proceedings of2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, (1), 450-457, 2009. http://dx.doi.org/10.1109/WI-IAT.2009.78
Xiong L., X. Chen, T. Huang, J. Schneider, J. G. Carbonell, Temporal collaborative filtering with Bayesian probabilistic tensor factorization, In Proceedings of SIAM International Conference on Data Mining, 2010.
Srebro N. and Jakkola T., Weighted low rank approximations, Proceedings of 20th International Conference on Machine Learning, 720-727, 2003.
Daniel D. Lee and H. Sebastian Seung, Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755): 788-791, 1999. http://dx.doi.org/10.1038/44565
Lan W. and Zhengjun Z., Collaborative filtering algorithm based on time weight, Journal of computer application, 27(9): 2302-2303, 2007.
Huaizhen Y. Xiaoqi C. and Meilian L., Research on the Personalized Recommendation Algorithm Based on Time Weight, Computer engineering& science, 31(6): 126-128, 2009.
Donghui L, Dewei P. and Hui Zh., Collaborative Filtering Algorithm Based on Time Weight and User's Feature, Journal of wuhan university of technology, 34(5): 144-148, 2012.
Smaranda Cosma, Mădălina Văleanu, Dan Cosma, Dana Vasilescu, Grigor Moldovan, Efficient Data Organisation in Distributed Computer Systems using Data Warehouse, INT J COMPUT COMMUN, 8(3): 367-373, 2013.
Kabir Golam and Hasin M. Ahsan Akhtar, Evaluation of customer oriented success factors in mobile commerce using fuzzy AHP, Journal of Industrial Engineering and Management, 4(2): 361-386, 2011.
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
