Open AccessNews Selection with Topic Modeling
Author(s) -
Çağrı Toraman
Publication year - 2013
Publication title -
electronic workshops in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 1477-9358
DOI - 10.14236/ewic/fdia2013.7
Subject(s) - computer science , popularity , selection (genetic algorithm) , topic model , news media , information retrieval , exploit , news aggregator , diversification (marketing strategy) , front page , recommender system , world wide web , data science , advertising , political science , artificial intelligence , media studies , marketing , law , sociology , business , computer security
There are numerous news articles coming to news aggregators and important news are selected to be presented on the front-page. There are two types of news selection for the front-page of news aggregators: personalized and public news recommendation (selection). This study examines public news recommendation that aims to satisfy all users’ interest on the front-page. Public news recommendation is mainly done by meta-features like news popularity. A different approach that exploits the news content is introduced in this work. The main target is to select important (significant) news articles while providing diversification in the selected news topics. A new approach based on topic modeling is developed for this purpose. Results show that it is hard to achieve satisfactory level of precision when content-based public news recommendation is applied. However, precision of topic modeling-based approach is noticeably better than precision of random news recommendation. Topics of selected news are also diversified by using topic modeling.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom