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Adaptive social recommendation in a multiple category landscape

Chen, Duanbing and Zeng, An and Cimini, Giulio and Zhang, Yi-Cheng Adaptive social recommendation in a multiple category landscape. The European Physical Journal B - Condensed Matter, 86 (2). ISSN 1434-6028 (2013)

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People in the Internet era have to cope with the information overload, striving to find what they are interested in, and usually face this situation by following a limited number of sources or friends that best match their interests. A recent line of research, namely adaptive social recommendation, has therefore emerged to optimize the information propagation in social networks and provide users with personalized recommendations. Validation of these methods by agent-based simulations often assumes that the tastes of users can be represented by binary vectors, with entries denoting users’ preferences. In this work we introduce a more realistic assumption that users’ tastes are modeled by multiple vectors. We show that within this framework the social recommendation process has a poor outcome. Accordingly, we design novel measures of users’ taste similarity that can substantially improve the precision of the recommender system. Finally, we discuss the issue of enhancing the recommendations’ diversity while preserving their accuracy.

Item Type: Article
Identification Number: 10.1140/epjb/e2012-30899-9
Uncontrolled Keywords: Statistical and Nonlinear Physics
Subjects: G Geography. Anthropology. Recreation > GT Manners and customs
H Social Sciences > HA Statistics
Research Area: Economics and Institutional Change
Depositing User: Caterina Tangheroni
Date Deposited: 06 Nov 2015 12:28
Last Modified: 06 Nov 2015 12:28
URI: http://eprints.imtlucca.it/id/eprint/2847

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