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A Social Recommendation Based on Metric Learning and Users’ Co-occurrence Pattern
Version 1
: Received: 28 September 2021 / Approved: 29 September 2021 / Online: 29 September 2021 (11:21:35 CEST)
A peer-reviewed article of this Preprint also exists.
Zhang, X.; Qin, J.; Zheng, J. A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern. Symmetry 2021, 13, 2158. Zhang, X.; Qin, J.; Zheng, J. A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern. Symmetry 2021, 13, 2158.
Abstract
For personalized recommender systems,matrix factorization and its variants have become mainstream in collaborative filtering.However,the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless,most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper,we propose a metric learning-based social recommendation model called SRMC.SRMC exploits users' co-occurrence pattern to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations.Experiments on three public datasets show that our model is more effective than the compared models.
Keywords
recommender systems; social recommendation; metric learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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