Abstract
Traditionally, content-based news recommendation is performed by means of the cosine similarity and the TF-IDF weighting scheme for terms occurring in news messages and user profiles. Semantics-driven variants like SF-IDF additionally take into account term meaning by exploiting synsets from semantic lexicons. However, semantics-based weighting techniques are not able to handle - often crucial - named entities, which are often not present in semantic lexicons. Hence, we extend SF-IDF by also employing named entity similarities using Bing page counts. Our proposed method, Bing-SF-IDF, outperforms TF-IDF and its semantics-driven variants in terms of F1-scores and kappa statistics.
| Original language | English |
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| Title of host publication | Tenth International Workshop on Web Information Systems Modeling (WISM 2013) at Thirty-Second International Conference on Conceptual Modeling (ER 2013) |
| Publisher | Springer-Verlag |
| Publication status | Published - 11 Nov 2013 |
Research programs
- EUR ESE 32