Learning User Similarity and Rating Style for Collaborative Recommendation

Lily F. Tian*, Kwok Wai Cheung

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication25th European Conference on IR Research, ECIR 2003, Pisa, Italy, April 14–16, 2003. Proceedings
EditorsFabrizio Sebastiani
PublisherSpringer Berlin Heidelberg
Number of pages11
ISBN (Electronic)9783540366188
ISBN (Print)9783540012740
Publication statusPublished - 2003
Event25th European Conference on IR Research, ECIR 2003 - Pisa, Italy
Duration: 14 Apr 200316 Apr 2003

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th European Conference on IR Research, ECIR 2003

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Collaborative filtering
  • Machine learning
  • Rating style
  • Recommender systems
  • User similarity


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