Eur. Phys. J. B (2013) 86: 61
https://doi.org/10.1140/epjb/e2012-30899-9

Regular Article

Adaptive social recommendation in a multiple category landscape

¹ Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
² Physics Department, University of Fribourg, Chemin du Musée 3, 1700 Fribourg, Switzerland

^a e-mail: an.zeng@unifr.ch

Received: 4 October 2012
Received in final form: 15 December 2012
Published online: 18 February 2013

Abstract

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.