A mathematical model for product selection strategies in a recommender system

Electronic commerce (EC) has become an important support for business. EC is regarded as an efficient platform to bridge the gape between the suppliers and consumers, but how to make use of a huge amount of transaction data and identify potential customers on the internet remains a challenge for an EC company. In particular, to recommend proper products to customers, the preferences of the targeted customers need to be accurately specified and their preferences should be taken into account. This is not only to show the goodwill of the company, but also to retain the customer relation. This study aims to construct a recommender system by focusing on the on-line decision support module with respect to customers' characteristics and supplier's profits. For effective decision support, a mathematical model is developed so that the right product can be recommended to the right person with the best profit for the company. A numerical example is used to illustrate how this model works when both supplier's and consumers' desires are taken into consideration to achieve an optimal Win-Win Strategy for market expansion.

[1]  Arthur M. Geoffrion,et al.  Exceptional Paper—Parametric and Postoptimality Analysis in Integer Linear Programming , 1977 .

[2]  Arnd Kohrs,et al.  Creating user-adapted Websites by the use of collaborative filtering , 2001, Interact. Comput..

[3]  Chi-Hoon Lee,et al.  Web personalization expert with combining collaborative filtering and association rule mining technique , 2001, Expert Syst. Appl..

[4]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[5]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[6]  Ping-Yu Hsu,et al.  Algorithms for mining association rules in bag databases , 2004, Inf. Sci..

[7]  Dennis G. Severance,et al.  Mathematical Techniques for Efficient Record Segmentation in Large Shared Databases , 1976, JACM.

[8]  Paolo Toth,et al.  Knapsack Problems: Algorithms and Computer Implementations , 1990 .

[9]  Josep Lluís de la Rosa i Esteva,et al.  A Taxonomy of Recommender Agents on the Internet , 2003, Artificial Intelligence Review.

[10]  Katia P. Sycara,et al.  WebMate: a personal agent for browsing and searching , 1998, AGENTS '98.

[11]  Duen-Ren Liu,et al.  Integrating AHP and data mining for product recommendation based on customer lifetime value , 2005, Inf. Manag..

[12]  Sergios Theodoridis,et al.  Pattern Recognition , 1998, IEEE Trans. Neural Networks.

[13]  John Riedl,et al.  Recommender systems in e-commerce , 1999, EC '99.

[14]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[15]  Arthur M. Geoffrion,et al.  Parametric and Postoptimality Analysis in Integer Linear Programming , 1976 .

[16]  Bing Xu,et al.  A personalized products selection assistance based on e-commerce machine learning , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[17]  M. Eben-Chaime.  Parametric solution for linear bicriteria knapsack models , 1996 .

[18]  Hsiao-Fan Wang,et al.  Managing customer profitability in a competitive market by continuous data mining , 2006 .

[19]  Vijay V. Raghavan,et al.  A critical analysis of vector space model for information retrieval , 1986 .