Personalized recommendation system based on product specification values

Abstract In this paper, we developed a recommendation system which enables bidirectional communication between the user and system using an utility range-based product recommendation algorithm in order to provide more dynamic and personalized recommendations. The system is based on an interactive procedure for recommending similar ones among the products of the collaborative companies that share the product taxonomy table. The main idea of the proposed procedure is using a multi-attribute decision making (MADM) to find the utility values of products in same product class of the companies. Based on the values, we determine what products are similar. The similar product recommendation system is a Web-based application system running on a PC. The system has a user-friendly graphic user interface to encode easily incomplete value judgments. Using the system, we carry out the experiments for performance evaluation of our procedure. The experimental study shows that the utility range-based approach is a viable solution to the similar product recommendation problems in the viewpoints of correct rate and satisfaction rate.

[1]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[2]  Yoon Ho Cho,et al.  An utility range-based similar product recommendation algorithm for collaborative companies , 2004, Expert Syst. Appl..

[3]  Deborah Kania,et al.  Internet World Guide to One-To-One Web Marketing , 1998 .

[4]  Yoon Ho Cho,et al.  A personalized recommender system based on web usage mining and decision tree induction , 2002, Expert Syst. Appl..

[5]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[6]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

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

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

[9]  Choochart Haruechaiyasak,et al.  Category cluster discovery from distributed WWW directories , 2003, Inf. Sci..

[10]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

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

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

[13]  Soung Hie Kim,et al.  An interactive procedure for multiple attribute group decision making with incomplete information: Range-based approach , 1999, Eur. J. Oper. Res..

[14]  Robert J. Schalkoff,et al.  Pattern recognition - statistical, structural and neural approaches , 1991 .