A novel recommendation method based on rough set and integrated feature mining

S. Tseng, Ja Hwung Su, Bo Wen Wang, Chin Yuan Hsiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The explosive growth of information makes people confused in making a choice among a huge amount of products, like movies, books, etc. To help people clarify what they want easily, in this study, we present an intelligent recommendation approach named RSCF (Recommendation by Rough-Set and Collaborative Filtering) that integrates collaborative information and content features to predict user preferences on the basis of rough-set theory. The contribution of this paper is that our proposed approach can completely solve the traditional problems occurring in recent studies, such as cold-star, first-rater, sparsity and scalability problems. The empirical evaluation results reveal that our proposed approach can reduce the gap between user's interest and recommended items more effectively than other existing approaches in terms of accuracy of recommendations.

Original languageEnglish
Title of host publication3rd International Conference on Innovative Computing Information and Control, ICICIC'08
DOIs
StatePublished - 30 Sep 2008
Event3rd International Conference on Innovative Computing Information and Control, ICICIC'08 - Dalian, Liaoning, China
Duration: 18 Jun 200820 Jun 2008

Publication series

Name3rd International Conference on Innovative Computing Information and Control, ICICIC'08

Conference

Conference3rd International Conference on Innovative Computing Information and Control, ICICIC'08
CountryChina
CityDalian, Liaoning
Period18/06/0820/06/08

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    Tseng, S., Su, J. H., Wang, B. W., & Hsiao, C. Y. (2008). A novel recommendation method based on rough set and integrated feature mining. In 3rd International Conference on Innovative Computing Information and Control, ICICIC'08 [4603519] (3rd International Conference on Innovative Computing Information and Control, ICICIC'08). https://doi.org/10.1109/ICICIC.2008.612