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http://dx.doi.org/10.13088/jiis.2011.17.3.147

New Collaborative Filtering Based on Similarity Integration and Temporal Information  

Choi, Keun-Ho (Business School, Korea University)
Kim, Gun-Woo (Department of Business Administration, Hanbat National University)
Yoo, Dong-Hee (Department of Electronics Engineering and Information Science, Korea Military Academy)
Suh, Yong-Moo (Business School, Korea University)
Publication Information
Journal of Intelligence and Information Systems / v.17, no.3, 2011 , pp. 147-168 More about this Journal
Abstract
As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.
Keywords
추천시스템;협업필터링;시간정보;유사도함수;
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