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ADORES: A Diversity-oriented Online Recommender System

Chevalier, Max and Dudognon, Damien and Mothe, Josiane ADORES: A Diversity-oriented Online Recommender System. (2016) In: 31st Annual ACM Symposium on Applied Computing (SAC 2016), 4 April 2016 - 8 April 2016 (Pise, Italy).

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Official URL: http://dx.doi.org/10.1145/2851613.2851921

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. Our proposals are validated through several experiments.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Association for Computing Machinery (ACM ) editor. The definitive version is available at http://dl.acm.org This papers appears in SAC'16 ISBN: 978-1-4503-3739-7 The original PDF is available at: http://dl.acm.org/citation.cfm?doid=2851613.2851921
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Overblog (FRANCE)
Laboratory name:
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Deposited By: IRIT IRIT
Deposited On:23 Jun 2017 07:19

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