THE ROLE OF TASTE AFFINITY IN AGENT-BASED MODELS FOR SOCIAL RECOMMENDATION

Authored by Giulio Cimini, An Zeng, Matus Medo, Duanbing Chen

Date Published: 2013-08

DOI: 10.1142/s0219525913500094

Sponsors: European Union Chinese National Natural Science Foundation

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

In the Internet era, online social media emerged as the main tool for sharing opinions and information among individuals. In this work, we study an adaptive model of a social network where directed links connect users with similar tastes, and over which information propagates through social recommendation. Agent-based simulations of two different artificial settings for modeling user tastes are compared with patterns seen in real data, suggesting that users differing in their scope of interests is a more realistic assumption than users differing only in their particular interests. We further introduce an extensive set of similarity metrics based on users' past assessments, and evaluate their use in the given social recommendation model with both artificial simulations and real data. Superior recommendation performance is observed for similarity metrics that give preference to users with small scope-who thus act as selective filters in social recommendation.

Tags

Agent-based modeling Taste similarity adaptive complex networks information diffusion and filtering social recommendation