Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Learning user similarity and rating style for collaborative recommendation

Language

English

Abstract

Information filtering is an area getting more important as we have long been flooded with too much information. Product brokering in e-commerce is a typical example and systems which can recommend products to their users in a personalized manner have been studied rigoriously in recent years. Collaborative filtering is one of the commonly used approaches where careful choices of the user similarity measure and the rating style representation are required, and yet there is no guarantee for their optimality. In this paper, we propose the use of machine learning techniques to learn the user similarity as well as the rating style. A criterion function measuring the prediction errors is used and several problem formulations are proposed together with their learning algorithms. We have evaluated our proposed methods using the EachMovie dataset and succeeded in obtaining significant improvement in recommendation accuracy when compared with the standard correlation method.

Information filtering is an area getting more important as we have long been flooded with too much information. Product brokering in e-commerce is a typical example and systems which can recommend products to their users in a personalized manner have been studied rigoriously in recent years. Collaborative filtering is one of the commonly used approaches where careful choices of the user similarity measure and the rating style representation are required, and yet there is no guarantee for their optimality. In this paper, we propose the use of machine learning techniques to learn the user similarity as well as the rating style. A criterion function measuring the prediction errors is used and several problem formulations are proposed together with their learning algorithms. We have evaluated our proposed methods using the EachMovie dataset and succeeded in obtaining significant improvement in recommendation accuracy when compared with the standard correlation method.

Keywords

Recommender systems, collaborative filtering, machine learning, user similarity, rating style

Publication Date

4-2003

Source Publication Title

Advances in Information Retrieval: 25th European Conference on IR Research, ECIR 2003, Pisa, Italy, April 14–16, 2003. Proceedings

Start Page

135

End Page

145

Series Title

Lecture Notes in Computer Science book series (LNCS, volume 2633)

Conference Location

Pisa, Italy

Publisher

Springer

Peer Reviewed

1

Copyright

© Springer-Verlag Berlin Heidelberg 2003

DOI

10.1007/3-540-36618-0_10

Link to Publisher's Edition

http://dx.doi.org/10.1007/3-540-36618-0_10

ISBN (print)

9783540012740

ISBN (electronic)

9783540366188

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