Abstract
Social media is an essential platform for sharing information between friends. But there are millions of users on social media with a lot of similar interests and hobbies. But it’s hard to find out identical quality friends. This is a challenging task to recommend friends not only considering mutual friends and geographical location but also based on their interests, tastes, and hobbies. Recommending friends should not be based solely on shared friends or location. In this paper, we will recommend friends based on similar interests, tastes, and hobbies. For this purpose, we will extract heterogeneous data from a user profile on which similarity depends and analyze users’ social media posts. Then apply different clustering algorithms in those features for finding out patterns. From our dataset after applying different unsupervised evaluation matrices, it is seen that for the number of clusters 3 our dataset is well separated. So, then the users who will be on the same cluster will be recommended to each other for various purposes.
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Roy, A.C., Mofakh Kharul Islam, A.S.M. (2023). Friend Recommendation System Based on Heterogeneous Data from Social Network. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. IJCACI 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1435-7_47
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DOI: https://doi.org/10.1007/978-981-99-1435-7_47
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