Automatic text processing
Fab: content-based, collaborative recommendation
Communications of the ACM
KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
Towards the web of concepts: extracting concepts from large datasets
Proceedings of the VLDB Endowment
Automatic keyphrase extraction by bridging vocabulary gap
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
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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 CON cept-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.