Improving recommendation based on features' co-occurrence effects in collaborative tagging systems

Hao Han, Yi Cai, Yifeng Shao, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users' attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user's favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment.

Original languageEnglish
Title of host publicationWeb Technologies and Applications - 14th Asia-Pacific Web Conference, APWeb 2012, Proceedings
Pages652-659
Number of pages8
DOIs
Publication statusPublished - 18 Apr 2012
Externally publishedYes
Event14th Asia Pacific Web Technology Conference, APWeb 2012 - Kunming, China
Duration: 11 Apr 201213 Apr 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7235 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Asia Pacific Web Technology Conference, APWeb 2012
CountryChina
CityKunming
Period11/04/1213/04/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this