Dynamic fuzzy clustering for recommender systems

Collaborative filtering is the most successful recommendation technique. In this paper, we apply the concept of time to collaborative filtering algorithm. We propose dynamic fuzzy clustering algorithm and apply it to collaborative filtering algorithm for dynamic recommendations. We add a time dimension to the original input data of collaborative filtering for finding the fuzzy cluster at different timeframes. We propose the dynamic degree of membership and determine the neighborhood for a given user based on the dynamic fuzzy cluster. The results of the evaluation experiment show the proposed model's improvement in making recommendations.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2005
Language
ENG
Citation

ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS BOOK SERIES: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, v.3518, pp.480 - 485

ISSN
0302-9743
URI
http://hdl.handle.net/10203/87514
Appears in Collection
KGSF-Journal Papers(저널논문)
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