Temporal Collaborative Filtering with Graph Convolutional Neural Networks

• URL: http://arxiv.org/abs/2010.06425v1
• Date: Tue, 13 Oct 2020 14:38:40 GMT
• Title: Temporal Collaborative Filtering with Graph Convolutional Neural Networks
• Authors: Esther Rodrigo Bonet, Duc Minh Nguyen and Nikos Deligiannis
• Abstract summary: Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems. Recent graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations. We propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics.
• Score: 21.75176876341124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods employ recurrent neural networks (RNNs) to model such aspects. These methods deploy matrix-factorization-based (MF-based) approaches to learn the user and item representations. Recently, graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations over traditional MF-based approaches in non-temporal CF settings. Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics. A challenge with this method lies in the increased data sparsity, which negatively impacts obtaining meaningful quality representations with GNNs. To overcome this challenge, we train a GNN model at each time step using a set of observed interactions accumulated time-wise. Comprehensive experiments on real-world data show the improved performance obtained by our method over several state-of-the-art temporal and non-temporal CF models.

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