Temporal collaborative filtering (TCF) methods aimat modelling non-static aspects behind recommender systems,such as the dynamics in users{\textquoteright} 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.
Rodrigo Bonet, E, Nguyen, MD & Deligiannis, N 2020, Temporal Collaborative Filtering with Graph Convolutional Neural Networks. in 25th International Conference on Pattern Recognition (ICPR)., 9413200, Proceedings - International Conference on Pattern Recognition, IEEE, pp. 4736-4742, 25th IEEE International Conference on Pattern Recognition, Milan, Italy, 10/01/21. https://doi.org/10.1109/ICPR48806.2021.9413200, https://doi.org/10.1109/ICPR48806.2021.9413200
Rodrigo Bonet, E., Nguyen, M. D., & Deligiannis, N. (2020). Temporal Collaborative Filtering with Graph Convolutional Neural Networks. In 25th International Conference on Pattern Recognition (ICPR) (pp. 4736-4742). Article 9413200 (Proceedings - International Conference on Pattern Recognition). IEEE. https://doi.org/10.1109/ICPR48806.2021.9413200, https://doi.org/10.1109/ICPR48806.2021.9413200
@inproceedings{718fd4744d8c4de5afed1705543f69c8,
title = "Temporal Collaborative Filtering with Graph Convolutional Neural Networks",
abstract = "Temporal collaborative filtering (TCF) methods aimat modelling non-static aspects behind recommender systems,such as the dynamics in users{\textquoteright} 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.",
author = "{Rodrigo Bonet}, Esther and Nguyen, {Minh Duc} and Nikolaos Deligiannis",
year = "2020",
doi = "10.1109/ICPR48806.2021.9413200",
language = "English",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "IEEE",
pages = "4736--4742",
booktitle = "25th International Conference on Pattern Recognition (ICPR)",
note = "25th IEEE International Conference on Pattern Recognition ; Conference date: 10-01-2021 Through 15-01-2021",
url = "http://www.icpr2020.it/",
}