Improving User Profiles for E-Commerce by Genetic Algorithms

Recommendation systems are widely adopted in e-commerce businesses for helping customers locate products they would like to purchase. The major challenge for these systems is bridging the gap between the physical characteristics of data with the users’ perceptions. In order to address this challenge, employing user profiles to improve accuracy becomes essential. However, the system performance may degrade due to inaccuracy of user profiles. Therefore, an effective system should offer learning mechanisms to correct erroneous user inputs. In this paper, we extend an existing recommendation system, Yoda, to improve the profiles automatically by utilizing users’ relevance feedback with genetic algorithms (GA). Our experimental results indicate that the retrieval accuracy is significantly increased by using the GA-based learning mechanism.

[1]  Farnoush Banaei Kashani,et al.  Feature Matrices: A Model for Efficient and Anonymous Web Usage Mining , 2001, EC-Web.

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  Cyrus Shahabi,et al.  Knowledge discovery from users Web-page navigation , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[4]  John Riedl,et al.  Combining Collaborative Filtering with Personal Agents for Better Recommendations , 1999, AAAI/IAAI.

[5]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[6]  Alexandros Moukas.  Amalthaea Information Discovery and Filtering Using a Multiagent Evolving Ecosystem , 1997, Appl. Artif. Intell..

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

[8]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[9]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

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

[11]  Ronald Fagin,et al.  Combining Fuzzy Information from Multiple Systems , 1999, J. Comput. Syst. Sci..

[12]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[13]  Brendan Kitts,et al.  Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities , 2000, KDD '00.

[14]  Ah-Hwee Tan,et al.  Learning user profiles for personalized information dissemination , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[15]  Dennis McLeod,et al.  Yoda: An Accurate and Scalable Web-Based Recommendation System , 2001, CoopIS.

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

[17]  Javed Mostafa,et al.  Detection of shifts in user interests for personalized information filtering , 1996, SIGIR '96.

[18]  Raymond T. Ng,et al.  Distance-based outliers: algorithms and applications , 2000, The VLDB Journal.

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Pattie Maes,et al.  Evolving agents for personalized information filtering , 1993, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications.