Abstract
A concept is a key phrase which can represent an entity, event or idea that people are interested in. Concept-centric Web news recommendation is a novel content-based recommendation paradigm which can partially alleviate the cold-start problem and provide better recommendation results in terms of diversity than traditional news recommendation systems, as it can capture users' interest in a natural way and can even recommend a new Web news to a user as long as it is conceptually relevant to a main concept of the Web news the user is browsing. This demonstration paper presents a novel CONcept-Centric nEws Recommendation sysTem called CONCERT. CONCERT consists of two parts: (1) A concept extractor which is based on machine learning algorithms and can extract main concepts from Web news pages, (2) A real-time recommender which recommends conceptually relevant Web news to a user based on the extracted concepts. © 2013 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Ren, H., & Feng, W. (2013). CONCERT: A concept-centric web news recommendation system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7923 LNCS, pp. 796–798). Springer Verlag. https://doi.org/10.1007/978-3-642-38562-9_82
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