With the ever growing amount of news on the Web, the need for automatically finding the relevant content increases. Semantics-driven news recommender systems suggest unread items to users by matching user profiles, which are based on information found in previously read articles, with emerging news. This paper proposes an extension to the state-of-the-art semantics-driven CF-IDF+ news recommender system, which uses identified news item concepts and their related concepts for constructing user profiles and processing unread news messages. Due to its domain specificity and reliance on knowledge bases, such a concept-based recommender neglects many highly frequent named entities found in news items, which contain relevant information about a news item’s content. Therefore, we extend the CF-IDF+ recommender by adding information found in named entities, through the employment of a Bing-based distance measure. Our Bing-CF-IDF+ recommender outperforms the classic TF-IDF and the concept-based CF-IDF and CF-IDF+ recommenders in terms of the F1 -score and the Kappa statistic.

Additional Metadata
Keywords Bing-CF-IDF+, Content-based recommender, Named entities, News recommendation system, Semantic Web
Persistent URL dx.doi.org/10.1007/978-3-030-21290-2_3, hdl.handle.net/1765/117388
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Citation
Brocken, E. (Emma), Hartveld, A. (Aron), de Koning, E. (Emma), van Noort, T. (Thomas), Hogenboom, F.P, Frasincar, F, & Robal, T. (Tarmo). (2019). Bing-CF-IDF+: A Semantics-Driven News Recommender System. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-030-21290-2_3