Dynamic learning of keyword-based preferences for news recommendation

被引：2
|
作者：
Moreno, Antonio [1 ]
Marin, Lucas [1 ]
Isern, David [1 ]
Perello, David [1 ]
机构：
[1] Univ Rovira & Virgili, ITAKA Res Grp, Dept Comp Sci & Math, E-43007 Tarragona, Catalonia, Spain
来源：
2014 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1 | 2014年
关键词：
recommender systems; user profile; preference learning; profile adaptation; SYSTEM;
D O I：
10.1109/WI-IAT.2014.55
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The accurate recommendation of daily news requires a detailed knowledge of the topics of interest to the user. The dynamic and continuous analysis of the content of the news that are read (or ignored) by the user every day may lead to the automatic, unsupervised and non-intrusive learning of the positive (and negative) preferences of the user with respect to a set of keywords. These preferences may then be used to rank the daily news, so that the user is recommended those items that match better with his/her interests. The cyclic preference learning methodology described in this paper is illustrated with a case example based on real news from the British newspaper The Guardian, in which promising results have been obtained.
引用
收藏
页码：347 / 354
页数：8
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