relation: http://eprints.imtlucca.it/2847/
title: Adaptive social recommendation in a multiple category landscape
creator: Chen, Duanbing
creator: Zeng, An
creator: Cimini, Giulio
creator: Zhang, Yi-Cheng
subject: GT Manners and customs
subject: HA Statistics
description: 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.
publisher: Springer
date: 2013
type: Article
type: PeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/2847/1/1210.1441v1.pdf
identifier:   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)  
relation: http://dx.doi.org/10.1140/epjb/e2012-30899-9
relation: 10.1140/epjb/e2012-30899-9