Dynamic Learning of Keyword-Based Preferences for News Recommendation

Author

Moreno, Alexander ; Marin, Luis ; Isern, David ; Perello, David

Author_Institution

Dept. of Comput. Sci. & Math., Univ. Rovira i Virgili, Tarragona, Spain

Volume

1

fYear

2014

fDate

11-14 Aug. 2014

Firstpage

347

Lastpage

354

Abstract

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.

Keywords

information resources; learning (artificial intelligence); publishing; recommender systems; user interfaces; British newspaper; The Guardian; continuous analysis; cyclic preference learning methodology; daily news; dynamic learning; keyword-based preferences; news recommendation; nonintrusive learning; Algorithm design and analysis; Collaboration; Equations; Frequency measurement; Heuristic algorithms; Recommender systems; preference learning; profile adaptation; recommender systems; user profile;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on

Conference_Location

Warsaw

Type

conf

DOI

10.1109/WI-IAT.2014.55

Filename

6927564