Optimal associative neighbor mining using attributes for ubiquitous recommendation systems

Authors:
Kyung-Yong Jung;Hee-Joung Hwang;Un-Gu Kang
Affiliations:
School of Computer Information Engineering, Sangji University, Korea;Department of Information Technology Engineering, Gachon University of Medicine and Science, Korea;Department of Information Technology Engineering, Gachon University of Medicine and Science, Korea
Venue:
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Year:
2006

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Abstract

Ubiquitous recommendation systems predict new items of interest for a user, based on predictive relationship discovered between the user and other participants in Ubiquitous Commerce. In this paper, optimal associative neighbor mining, using attributes, for the purpose of improving accuracy and performance in ubiquitous recommendation systems, is proposed. This optimal associative neighbor mining selects the associative users that have similar preferences by extracting the attributes that most affect preferences. The associative user pattern comprising 3-AUs (groups of associative users composed of 3-users), is grouped through the ARHP algorithm. The approach is empirically evaluated, for comparison with the nearest-neighbor model and k-means clustering, using the MovieLens datasets. This method can solve the large-scale dataset problem without deteriorating accuracy quality.