Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

被引:194
作者
Lee, Seok Kee [2 ]
Cho, Yoon Ho [1 ]
Kim, Soung Hie [2 ]
机构
[1] Kookmin Univ, Sch Business Adm, Seoul 136702, South Korea
[2] Korea Adv Inst Sci & Technol, Grad Sch Management, Seoul 130012, South Korea
关键词
Collaborative filtering; Recommender system; Mobile web usage mining; Consensus model; PERSONALIZATION; ASSOCIATION; ALLEVIATE; RANKING; SYSTEM;
D O I
10.1016/j.ins.2010.02.004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Implicit ratings are more desirable for the mobile Web, but commonly used cardinal (interval, ratio) scales for representing preferences are also unsatisfactory because they may increase estimation errors. In this paper, we propose a CF-based recommendation methodology based on both implicit ratings and less ambitious ordinal scales. A mobile Web usage mining (mWUM) technique is suggested as an implicit rating approach, and a specific consensus model typically used in multi-criteria decision-making (MCDM) is employed to generate an ordinal scale-based customer profile. An experiment with the participation of real mobile Web customers shows that the proposed methodology provides better performance than existing CF algorithms in the mobile Web environment. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码:2142 / 2155
页数:14
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