A Semantic Approach for News Recommendation

Flavius Frasincar, W IJntema, F Goossen, Frederik Hogenboom

Research output: Chapter/Conference proceedingChapterAcademic

Abstract

News items play an increasingly important role in the current business decision processes. Due to the large amount of news published every day it is difficult to find the new items of one’s interest. One solution to this problem is based on employing recommender systems. Traditionally, these recommenders use term extraction methods like TF-IDF combined with the cosine similarity measure. In this chapter, we explore semantic approaches for recommending news items by employing several semantic similarity measures. We have used existing semantic similarities as well as proposed new solutions for computing semantic similarities. Both traditional and semantic recommender approaches, some new, have been implemented in Athena, an extension of the Hermes news personalization framework. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the new semantic recommenders have a better F-measure than existing semantic recommenders.
Original languageEnglish
Title of host publicationBusiness Intelligence Applications and the Web: Models, Systems and Technologies
EditorsM.E. Zorrilla, J.-N. Mazón, Ó. Ferrández, I. Garrigós, F. Daniel, J. Trujillo
PublisherIGI Global
Pages102-121
Number of pages20
Publication statusPublished - 2011

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