A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation

Abstract : How can we effectively recommend items to a user about whom we have no information? This is the problem we focus on, known as the cold-start problem. In this paper, we focus on the cold user problem. In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information? Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS. In comparison with two baselines, PdMS improves the performance as measured by the nDCG. These improvements are demonstrated on real, public datasets.
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Communication dans un congrès
25th ACM Conference on User Modelling, Adaptation and Personalization (UMAP), Jul 2017, Bratislava, Slovakia. 2017, 〈http://www.um.org/umap2017/〉
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  • HAL Id : hal-01517967, version 1

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Crícia Felício, Klérisson Paixão, Celia Barcelos, Philippe Preux. A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation. 25th ACM Conference on User Modelling, Adaptation and Personalization (UMAP), Jul 2017, Bratislava, Slovakia. 2017, 〈http://www.um.org/umap2017/〉. 〈hal-01517967〉

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