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Communication Dans Un Congrès Année : 2016

ADORES: A Diversity-oriented Online Recommender System

ADORES

Résumé

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. Our proposals are validated through several experiments.
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Dates et versions

hal-04109518 , version 1 (30-05-2023)

Identifiants

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Max Chevalier, Damien Dudognon, Josiane Mothe. ADORES: A Diversity-oriented Online Recommender System. 31st Annual ACM Symposium on Applied Computing (SAC 2016), Apr 2016, Pise, Italy. pp.1075-1076, ⟨10.1145/2851613.2851921⟩. ⟨hal-04109518⟩
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