Socially Aware Conference Participant Recommendation With Personality Traits | IEEE Journals & Magazine | IEEE Xplore

Socially Aware Conference Participant Recommendation With Personality Traits


Abstract:

As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations ...Show More

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 have not 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 aforementioned recommendation entities, SPARP then recommends participants to each other for effective collaborations. We evaluate SPARP using a relevant data set. Experimental results confirm that SPARP is reliable and outperforms other state-of-the-art methods.
Published in: IEEE Systems Journal ( Volume: 11, Issue: 4, December 2017)
Page(s): 2255 - 2266
Date of Publication: 13 August 2014

ISSN Information:


I. Introduction

NOWADAYS, recommender systems have substantiated their necessity and importance because of how they objectively focus on solving information overload problems of users. Recommender systems provide users with personalized information services that are sometimes proactive. Due to their potential value and associated greatness in terms of research, recommender systems are studied in both academia and industry.

References

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