Socially-Aware Conference Participant Recommendation with Personality Traits
Feng Xia, Nana Yaw Asabere, Haifeng Liu, Zhen Chen, and Wei Wang

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…
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