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Conference Publication

Temporal Collaborative Filtering with Graph Convolutional Neural Networks

Host Publication: International Conference on Pattern Recognition (ICPR)

Authors: E. Rodrigo, D. Nguyen and N. Deligiannis

Publication Year: 2020


Abstract:

Temporal collaborative filtering (TCF) methods aimat modelling non-static aspects behind recommender systems,such as the dynamics in users’ preferences and social trendsaround items. State-of-the-art TCF methods employ recurrentneural networks (RNNs) to model such aspects. These methodsdeploy matrix-factorization-based approaches to learn the userand item representations. Recently, graph-neural-network-based(GNN-based) approaches have shown improved performance inproviding accurate recommendations over traditional MF-basedapproaches in non-temporal CF settings. Motivated by this, wepropose a novel TCF method that leverages GNNs to learn userand item representations and RNNs to model their temporaldynamics. A challenge with this method lies in the increaseddata sparsity, which makes it more complicated to obtain qualityrepresentations with GNNs. To overcome this challenge, wetrain a GNN model at each time step using a set of observedinteractions accumulated time-wise. Comprehensive experimentson real-world data show the improved performance obtainedby our method over several state-of-the-art temporal and nontemporalCF models.

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Miss Esther Rodrigo

+32 (0)2 629 2930

erodrigo@etrovub.be

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Prof. Dr. Ir. Nikolaos Deligiannis

+32 (0)02 629 168

ndeligia@etrovub.be

more info

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