Max Chevalier
ABSTRACT
Browsing a content platform usually does not require a user identification. In this context, personalized approaches can not be used since no information related to the user is available. In that case, it is important to consider the variety of potential interests of users when providing recommendations. In this paper, we propose a scalable recommendation diversity-oriented model which considers solely the current visited document, the available collection and the past clickthrough documents to produce a list of diversified recommendations. A learning phase is integrated to improve recommendation relevance along time. Our proposals are validated through several experiments.
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Digital Library
- L. Candillier, M. Chevalier, D. Dudognon, and J. Mothe. Multiple similarities for diversity in recommender systems. International Journal On Advances in Intelligent Systems, 5(3 and 4):234--246, 2012.Google Scholar
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Index Terms
- ADORES: a diversity-oriented online recommender system
Recommendations
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