Dynamic Learning of Keyword-Based Preferences for News Recommendation
Antonio Moreno, Lucas Marín, David Isern, David Perello · 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) · 2014
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.