HPRS: A profitability based recommender system

In electronic commerce, recommender systems are popularly being used to help enterprises for satisfying customers' individually diverse preferences. These systems learn about user preferences over time and automatically suggest products that fit the learned model of user preferences. In tradition, recommendations are provided to customers based on purchase probability and customers' preferences, without considering the profitability factor for sellers. This work presents a new profitability-based recommender system, HPRS (hybrid perspective recommender system), which attempts to integrate the profitability factor into the traditional recommender systems. Comparisons between our proposed system and traditional system which only considers the purchase probability clarify the advantages of our system. The experimental results show that the proposed HPRS can increase profit from cross-selling without compromising recommendation accuracy.

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

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

[3]  James T. Kwok,et al.  Mining customer product ratings for personalized marketing , 2003, Decis. Support Syst..

[4]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[5]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[6]  Vladimir Kotlyar,et al.  Personalization of Supermarket Product Recommendations , 2004, Data Mining and Knowledge Discovery.

[7]  Chin-Feng Lee,et al.  On-line personalized sales promotion in electronic commerce , 2004, Expert Syst. Appl..

[8]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[9]  Ying-Feng Kuo,et al.  Personalization technology application to Internet content provider , 2001, Expert Syst. Appl..

[10]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[11]  Yoon Ho Cho,et al.  A personalized recommendation procedure for Internet shopping support , 2002, Electron. Commer. Res. Appl..

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

[13]  Gye-Young Kim,et al.  A Web-based collaborative filtering system , 2003, Pattern Recognit..

[14]  F. Reichheld.  Loyalty-based management. , 1993, Harvard business review.

[15]  James Rucker,et al.  Siteseer: personalized navigation for the Web , 1997, CACM.

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

[17]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[18]  Chein-Shung Hwang,et al.  A Collaborative Recommender System Based on User Association Clusters , 2005, WISE.

[19]  Thomas Reutterer,et al.  An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data , 2003 .

[20]  Eunseok Lee,et al.  Semantic Web Recommender System Based Personalization Service for User XQuery Pattern , 2005, WINE.

[21]  Chih-Hung Liu,et al.  Intelligent agent-based systems for personalized recommendations in Internet commerce , 2002, Expert Syst. Appl..

[22]  Wei-Pang Yang,et al.  A New Content-Based Access Method for Video Databases , 1999, Inf. Sci..

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

[24]  F. F. Reichheld,et al.  Zero defections: quality comes to services. , 1990, Harvard business review.

[25]  Yoon Ho Cho,et al.  Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce , 2004, Expert Syst. Appl..

[26]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[27]  Fan Yang,et al.  A scalable P2P recommender system based on distributed collaborative filtering , 2004, Expert Syst. Appl..

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

[29]  R. Kohli,et al.  Internet Recommendation Systems , 2000 .

[30]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

[31]  Loren Terveen,et al.  PHOAKS: a system for sharing recommendations , 1997, CACM.

[32]  Ingoo Han,et al.  Detection of the customer time-variant pattern for improving recommender systems , 2005, Expert Syst. Appl..