A user-item predictive model for collaborative filtering recommendation

Heung Nam Kim, Ae Ttie Ji, Cheol Yeon, Geun Sik Jo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Collaborative Filtering recommender systems, one of the most representative systems for personalized recommendations in E-commerce, enable users to find the useful information easily. But traditional CF suffers from some weaknesses: scalability and real-time performance. To address these issues, we present a novel model-based CF approach to provide efficient recommendations. In addition, we propose a new method of building a model with dynamic updates, when users present explicit feedback. The experimental evaluation on MovieLens datasets shows that our method offers reasonable prediction quality as good as the best of user-based Pearson correlation coefficient algorithm.

Original languageEnglish
Title of host publicationUser Modeling 2007 - 11th International Conference, UM 2007, Proceedings
PublisherSpringer Verlag
Pages324-328
Number of pages5
ISBN (Print)9783540730774
DOIs
StatePublished - 2007
Event11th International on User Modeling Conference, UM 2007 - Corfu, Greece
Duration: 25 Jun 200729 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4511 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International on User Modeling Conference, UM 2007
Country/TerritoryGreece
CityCorfu
Period25/06/0729/06/07

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