Hybrid recommendation approaches: Collaborative filtering via valuable content information

Ya Yueh Shih, Duen-Ren Liu

Research output: Contribution to journalConference articlepeer-review

30 Scopus citations

Abstract

Collaborative filtering (CF) method has been successfully used in recommender systems to support product recommendation, but it has several limitations. This work uses customer demands derived from the frequent purchased products in each industry as valuable content information. Accordingly, this work explores two hybrid approaches each of which combines CF and customer demands to improve quality of recommendation. Valuable content information is also included as a factor in making recommendations for re-ranking candidate products. The experimental results indicate that the quality of recommendation obtained by the combined methods is promising.

Original languageEnglish
Number of pages1
JournalProceedings of the Annual Hawaii International Conference on System Sciences
DOIs
StatePublished - 10 Nov 2005
Event38th Annual Hawaii International Conference on System Sciences - Big Island, HI, United States
Duration: 3 Jan 20056 Jan 2005

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