Feature-based prediction of unknown preferences for nearest-neighbor collaborative filtering

Hyungil Kim, Juntae Kim, Jonathan Herlocker

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

7 Scopus citations

Abstract

Recommendation systems analyze user preferences and recommend items to a user by predicting the user's preference for those items. Among various kinds of recommendation methods, collaborative filtering (CF) has been widely used and successfully applied to practical applications. However, collaborative filtering has two inherent problems: data sparseness and the cold-start problems. In this paper, we propose a method of integrating additional feature information of users and items into CF to overcome the difficulties caused by sparseness and improve the accuracy of recommendation. Several experimental results that show the effectiveness of the proposed method are also presented.

Original languageEnglish
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
EditorsR. Rastogi, K. Morik, M. Bramer, X. Wu
Pages435-438
Number of pages4
StatePublished - 2004
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: 1 Nov 20044 Nov 2004

Publication series

NameProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Conference

ConferenceProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Country/TerritoryUnited Kingdom
CityBrighton
Period1/11/044/11/04

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