Socially aware conference participant recommendation with personality traits.

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dc.contributor.author Xia, F.
dc.contributor.author Asabere, N. Y.
dc.contributor.author Liu, H.
dc.contributor.author Chen, Z.
dc.contributor.author Wang, W.
dc.date.accessioned 2023-01-27T09:45:40Z
dc.date.available 2023-01-27T09:45:40Z
dc.date.issued 2017
dc.identifier.other 10.1109/JSYST.2014.2342375
dc.identifier.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6877610
dc.identifier.uri http://atuspace.atu.edu.gh:8080/handle/123456789/2705
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher IEEE Systems Journal en_US
dc.relation.ispartofseries vol.;11
dc.subject Collaboration en_US
dc.subject Personality en_US
dc.subject Recommender systems en_US
dc.subject Smart conference en_US
dc.subject Social awareness en_US
dc.title Socially aware conference participant recommendation with personality traits. en_US
dc.type Article en_US


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