Abstract
The emergence of social networking has led people to stay connected with friends, family, customers, colleagues or clients. Social networking can have social purposes, business purposes or both through sites such as Facebook, Instagram, LinkedIn, Twitter and many more. Recently, a large active social involvement have been seen from all echelons of society which keeps the friend circle increasing than never before. But, the friend suggestions based on one’s friend list or profile may not be appropriate in some situations. Considering this problem, in this paper, a Friend Suggestion System, FAFinder (Friend Affinity Finder) based on 5 major dimensions (attributes): Agreeableness, Conscientiousness, Extraversion, Emotional range and Openness is proposed. This will help in understanding more about the commonalities that one shares with the other on the basis of their behaviour, choices, likes and dislikes etc. The suggested list of friends are extracted from the People Database (containing details of the 5 dimensions of different people) by deploying the concept of Hellinger-Bhattacharyya Distance (H-B Distance) as a measure of dissimilarity between two people.
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https://github.com/ns3-jalpaiguri/Friend_Affinity_Finder.git
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Chakrabarty, N., Chowdhury, S., Kanni, S.D., Mukherjee, S. (2020). FAFinder: Friend Suggestion System for Social Networking. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_6
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DOI: https://doi.org/10.1007/978-3-030-34080-3_6
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