With the development of E-commerce, Recommendation Systems are applied more widely to guide the customers to search for their interested products. A recommendation system includes a user model, a recommended model and a recommendation algorithm. Limited resource, data valid time and cold start problems are not well considered in existing E-commerce recommendation system. This paper proposes a limited resource based algorithm to provide an improvement to the existing product recommendation algorithm and also provides a solution to cold start problem.

Limited resource, cold start, recommendation system.

[1] B.Sarwar, G.Karypis, J.Konstan, J.Riedl, “Analysis of recommendation algorithms for E-commerce”, In: ACM Conference on Electronic Commerce, pp.158-167, 2000.
[2] Billsus D., and Pazzani M. J., “Learning Collaborative Information Filters”, In Proceedings of ICML ’98, pp. 46-53, 1998.
[3]ZhiminChen, Yi Jiang, Yao Zhao, “A Collaborative Filtering Recommendation Algorithm Based on User Interest Change and Trust Evaluation”,International Journal of Digital Content Technology and its Application,vol.4,November 9, December2010.
[4] G.Karypis, “Evaluation of item-based top-N recommendation algorithms,” in Proc. of CIKM 2001, pp. 247–254, 2001.
[5] ShikharKesarwani,AsthaGoel and Dr.NeetuSardana,”MSD-Apriori:Discovering Borderline-rare items using Association Mining”,In proceedings of 2017 Tenth International Conference on Contemporary Computing (IC3),10-12 August 2017,Noida,India.
[6] Herlocker L. J，KonstanA.J，Terveen G. L，et a1, “Evaluating collaborative filtering recommender systems”, ACM Transactionon Information Systems，vol. 22, no.1, pp. 5-53, 2004.
[7]Basu, C., Hirsh, H., and Cohen, W. (1998). Recommendation as Classification: Using Social and Content-based Information in Recommendation. In Recommender System Workshop ’98. pp. 11-15.
[8] Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. (1992). Using Collaborative Filtering to Weave an Information Tapestry.Communications of the ACM.December.
[9]Xue, W., B. Xiao, et al. (2015). Intelligent mining on purchase information and recommendation system for e-commerce.2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).
[10] Goldberg D, Nichols D, Oki B M，et a1, “Using collaborative filtering to weave fin information Tapestry”, Communications of the ACM，vol. 35, no.12, pp. 61-70, 1992.
[11] Li, D., G. Zhao, et al. (2015). A Method of Purchase Prediction Based on User Behavior Log. 2015 IEEE International Conference on Data Mining Workshop (ICDMW).
[12] Ramezani, M. and F. Yaghmaee (2016). "A novel video recommendation system based on efficient retrieval of human actions."Physica a-Statistical Mechanics and Its Applications 457: 607-623.
[13] Yi, Z., D. Wang, et al. (2015). Purchase Behavior Prediction in MCommercewith an Optimized Sampling Methods. 2015 IEEE International Conference on Data Mining Workshop (ICDMW).
[14] Schafer, J. B., Konstan, J., and Riedl, J. (1999). Recommender Systems in E-Commerce. In Proceedings of ACM E-Commerce 1999 conference.
[15] Hill, W., Stead, L., Rosenstein, M., and Furnas, G. (1995). Recommending and Evaluating Choices in a Virtual Community of Use. In Proceedings of CHI ’95