This work proposes the Bing-CSF-IDF+ recommender – a content-based recommender that makes use of semantic relationships, and combines the best features of our earlier introduced Bing-SF-IDF+ and CF-IDF+ systems. First, we make use of concepts and concept relationships from a domain ontology. Next, Bing-CSF-IDF+ employs the synsets and synset relationships from a semantic lexicon that have not been previously captured by the domain ontology. Last, named entities and their frequencies as provided by Bing – not present in the semantic lexicon and domain ontology – are utilized. Our experiments show that Bing-CSF-IDF+ significantly outperforms Bing-SF-IDF+ and CF-IDF+ on scores and Kappa statistics based on a news data set.

Additional Metadata
Persistent URL dx.doi.org/10.1007/978-3-030-54623-6_13, hdl.handle.net/1765/130078
Series Communications in Computer and Information Science
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Citation
van Huijsduijnen, L.H. (Lies Hooft), Hoogmoed, T. (Thom), Keulers, G. (Geertje), Langendoen, E. (Edmar), Langendoen, S. (Sanne), Vos, T. (Tim), … Robal, T. (Tarmo). (2020). Bing-CSF-IDF+: A Semantics-Driven Recommender System for News. In Communications in Computer and Information Science. doi:10.1007/978-3-030-54623-6_13