The role of taste affinity in agent-based models for social recommendation

TL;DR

This paper investigates how taste affinity influences social recommendation in online social networks, demonstrating that models favoring users with narrow interests improve recommendation accuracy based on simulations and real data.

Contribution

It introduces an agent-based model incorporating taste affinity and evaluates various similarity metrics, highlighting the effectiveness of preferences for users with small interest scopes.

Findings

Preference for users with small interest scope improves recommendation performance

Similarity metrics based on past assessments enhance social recommendation accuracy

Model aligns well with patterns observed in real social media data

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.