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Open AccessArticle

User Similarity Determination in Social Networks

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Department of Computer Science & Engineering, Narowal Campus, University of Engineering & Technology Lahore, Lahore 51600, Pakistan
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Department of Computer Science & Engineering, University of Engineering & Technology Lahore, Lahore 54890, Pakistan
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Author to whom correspondence should be addressed.
Technologies 2019, 7(2), 36; https://doi.org/10.3390/technologies7020036
Received: 8 March 2019 / Revised: 1 April 2019 / Accepted: 3 April 2019 / Published: 15 April 2019
(This article belongs to the Special Issue Next Generation of Recommender Systems)
Online social networks have provided a promising communication platform for an activity inherently dear to the human heart, to find friends. People are recommended to each other as potential future friends by comparing their profiles which require numerical quantifiers to determine the extent of user similarity. From similarity-based methods to artificial intelligent machine learning methods, several metrics enable us to characterize social networks from different perspectives. This research focuses on the collaborative employment of neighbor based and graphical distance-based similarity measurement methods with text classification tools such as the feature matrix and feature vector. Likeminded nodes are predicted accurately and effectively as compared to other methods. View Full-Text
Keywords: neighbour based similarity metrics; Euclidean distance; distance based similarity metrics; similarity score vector; data objects; adjacency matrix; link formation; node profile attributes neighbour based similarity metrics; Euclidean distance; distance based similarity metrics; similarity score vector; data objects; adjacency matrix; link formation; node profile attributes
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Tariq, S.; Saleem, M.; Shahbaz, M. User Similarity Determination in Social Networks. Technologies 2019, 7, 36.

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Technologies, EISSN 2227-7080, Published by MDPI AG
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