Attribute selection-based recommendation framework for short-head user group: An empirical study by MovieLens and IMDB

Title
Attribute selection-based recommendation framework for short-head user group: An empirical study by MovieLens and IMDB
Author(s)
정재은
Keywords
INFORMATION-RETRIEVAL; SOCIAL COLLABORATIONS; QUERY TRANSFORMATION; MODEL; SYNCHRONIZATION; NETWORKS; SYSTEMS
Issue Date
201203
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS, v.39, no.4, pp.4049 - 4054
Abstract
Most of recommender systems have serious difficulties on providing relevant services to the "short-head" users who have shown intermixed preferential patterns. In this paper, we assume that such users (which are referred to as long-tail users) can play an important role of information sources for improving the performance of recommendation. Attribute reduction-based mining method has been proposed to efficiently select the long-tail user groups. More importantly, the long-tail user groups as domain experts are employed to provide more trustworthy information. To evaluate the proposed framework, we have integrated MovieLens dataset with IMDB, and empirically shown that the long-tail user groups are useful for the recommendation process. (C) 2011 Elsevier Ltd. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/29553http://dx.doi.org/10.1016/j.eswa.2011.09.096
ISSN
0957-4174
Appears in Collections:
공과대학 > 컴퓨터공학과 > Articles
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