  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.
