Abstract
This paper proposes to exploit author-defined tags and social interaction data (commenting and sharing news items) in news recommendation. Moreover it presents a hybrid news recommender which suggest news items on the basis of the reader’s short and long-term reading history, taking reading trends and short-term interests into account. The experimental results we carried out provided encouraging results about the accuracy of the recommendations.
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Ardissono, L., Petrone, G., Vigliaturo, F. (2015). News Recommender Based on Rich Feedback. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science(), vol 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_27
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DOI: https://doi.org/10.1007/978-3-319-20267-9_27
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