Socially-Aware Conference Participant Recommendation with Personality Traits

TL;DR

This paper introduces SPARP, a novel recommendation algorithm that leverages personality traits and social relationships to improve participant matching at smart conferences, demonstrating superior performance over existing methods.

Contribution

The paper presents SPARP, the first algorithm to incorporate personality and social characteristics for conference participant recommendation, enhancing collaboration potential.

Findings

SPARP outperforms state-of-the-art recommendation methods.

Personality traits significantly improve recommendation accuracy.

Social and personality features together enhance participant matching.

Abstract

As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants. Recent research has shown that the personality traits of users can be used as innovative entities for effective recommendations. Nevertheless, subjective perceptions involving the personality of participants at smart conferences are quite rare and haven't gained much attention. Inspired by the personality and social characteristics of users, we present an algorithm called Socially and Personality Aware Recommendation of Participants (SPARP). Our recommendation methodology hybridizes the computations of similar interpersonal relationships and personality traits among participants. SPARP models the personality and social characteristic profiles of participants at a smart conference. By combining the above recommendation entities, SPARP then recommends participants to each other for effective collaborations. We evaluate SPARP using a relevant dataset. Experimental results confirm that SPARP is reliable and outperforms other state-of-the-art methods.