Title:
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Switching hybrid for cold-starting context-aware recommender systems
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Author:
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Braunhofer, Matthias; Codina Busquet, Víctor; Ricci, Francesco
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Other authors:
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Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
Abstract:
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Finding effective solutions for cold-starting Context-Aware Recommender Systems (CARSs) is important because usually low quality recommendations are produced for users, items or contextual situations that are new to the system. In this paper, we tackle this problem with a switching hybrid solution that exploits a custom selection of two CARS algorithms, each one suited for a particular cold-start situation, and switches between these algorithms depending on the detected recommendation situation (new user, new item or new context). We evaluate the proposed algorithms in an off-line experiment by using various contextually-tagged rating datasets. We illustrate some significant performance differences between the considered algorithms and show that they can be effectively combined into the proposed switching hybrid to cope with different types of cold-start problems. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Cold-start (Computing) -Cold-start problem -Context-aware recommender systems -Switching hybrid system -Inici fred (Informàtica) |
Rights:
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Document type:
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Article - Published version Conference Object |
Published by:
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Association for Computing Machinery (ACM)
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