One-Class Recommendation through Unsupervised Graph Neural Networks for Link Prediction
Resumo
Recommender systems play a key role in every online platform to provide users a better experience. Many classic recommendation approaches might find issues, mainly modeling user relations. Graphs can naturally model these relations since we can connect users interacting with items. On the other hand, when we model user-item relations through graphs, we do not have interactions between all users and items. In addition, there are few non-recommendation interactions, which makes it challenging to cover this scope. Also, the scope of what will not be recommended for the user is greater than what will be recommended. An alternative is One-Class Learning (OCL) which is able to recommend or not an item for a user only to train with recommendations, mitigating the needing to cover the scope of non-recommendations. However, OCL and Recommender Systems need appropriate, adequate, and robust representations to perform the recommendations in the best possible way. Therefore, we propose the one-class recommendation via representations learned by unsupervised graph neural networks (GNNs) for link prediction to generate a more robust and meaningful representation of users and items. In the results, our GNNs for link prediction outperform other methods to represent the users and items in the one-class recommendation. Furthermore, our proposal also outperforms a GNN for link prediction. Thus, our proposal recommended better and learned more robust representations.
Palavras-chave:
One-Class Learning, Recommender Systems, Graph Neural Networks, Link Prediction
Referências
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Rana, A., D’Addio, R. M., Manzato, M. G., and Bridge, D. Extended recommendation-by-explanation. User Modeling and User-Adapted Interaction 32 (1): 91–131, 2022.
Rossi, R. G., Lopes, A. A., and Rezende, S. O. A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification. In Proceedings of the 29th annual acm symposium on applied computing. ACM, Gyeongju Republic of Korea, pp. 79–84, 2014.
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Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., and Le, Q. V. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems vol. 32, pp. 5753–5763, 2019.
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Zhao, T., McAuley, J., and King, I. Improving latent factor models via personalized feature projection for one class recommendation. In Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, Melbourne, Australia, pp. 821–830, 2015.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL 2019: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minnesota, pp. 4171–4186, 2019.
do Carmo, P. and Marcacini, R. Embedding propagation over heterogeneous event networks for link prediction. In 2021 IEEE International Conference on Big Data (Big Data). IEEE, online, pp. 4812–4821, 2021.
Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., and Herrera, F. Learning from imbalanced data sets. Vol. 11. Springer, Switzerland, 2018.
Gôlo, M., Caravanti, M., Rossi, R., Rezende, S., Nogueira, B., and Marcacini, R. Learning textual representations from multiple modalities to detect fake news through one-class learning. In Proceedings of the Brazilian Symposium on Multimedia and the Web. ACM, Belo Horizonte, MG, Brazil, pp. 197–204, 2021.
Gôlo, M. P., Araújo, A. F., Rossi, R. G., and Marcacini, R. M. Detecting relevant app reviews for software evolution and maintenance through multimodal one-class learning. Information and Software Technology vol. 151, pp. 106998, 2022.
Gôlo, M. P. S., Rossi, R. G., and Marcacini, R. M. Learning to sense from events via semantic variational autoencoder. Plos one 16 (12): e0260701, 2021.
He, R. and McAuley, J. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web. ACM, Republic and Canton of Geneva, Switzerland, pp. 507–517, 2016.
Islam, K., Aridhi, S., and Sma ̄ıl-Tabbone, M. A comparative study of similarity-based and gnn-based link prediction approaches. In GEM (Graph Embedding and Mining) workshop, ECML-PKDD 2020. HAL, Ghent, Belgium., 2020.
Khoali, M., Laaziz, Y., Tali, A., and Salaudeen, H. A survey of one class e-commerce recommendation system techniques. Electronics 11 (6): 878, 2022.
Li, X. and Chen, H. Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach. Decision Support Systems 54 (2): 880–890, 2013.
Otter, D., Medina, J., and Kalita, J. A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems 32 (2): 604–624, 2020.
Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., and Yang, Q. One-class collaborative filtering. In 2008 Eighth IEEE International Conference on Data Mining. IEEE, Pisa, Italy, pp. 502–511, 2008.
Rana, A., D’Addio, R. M., Manzato, M. G., and Bridge, D. Extended recommendation-by-explanation. User Modeling and User-Adapted Interaction 32 (1): 91–131, 2022.
Rossi, R. G., Lopes, A. A., and Rezende, S. O. A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification. In Proceedings of the 29th annual acm symposium on applied computing. ACM, Gyeongju Republic of Korea, pp. 79–84, 2014.
Ru, S., Zhang, B., Jie, Y., Zhang, C., Wei, L., and Gu, C. Graph neural networks for privacy-preserving recommendation with secure hardware. In 2021 International Conference on Networking and Network Applications (NaNA). IEEE, Lijiang City, China, pp. 395–400, 2021.
Tax, D. and Duin, R. Support vector data description. Machine Learning 54 (1): 45–66, 2004.
Tax, D. M. J. One-class classification: concept-learning in the absence of counter-examples. Ph.D. thesis, Delft University of Technology, 2001.
Wu, S., Sun, F., Zhang, W., Xie, X., and Cui, B. Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR) just accepted (just accepted): 1–37, 2020.
Wu, W., Li, B., Luo, C., and Nejdl, W. Hashing-accelerated graph neural networks for link prediction. In Proceedings of the Web Conference 2021. ACM, Ljubljana Slovenia, pp. 2910–2920, 2021.
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32 (1): 4–24, 2020.
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., and Le, Q. V. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems vol. 32, pp. 5753–5763, 2019.
Zhang, M. and Chen, Y. Link prediction based on graph neural networks. Advances in neural information processing systems vol. 31, pp. 11, 2018.
Zhao, T., McAuley, J., and King, I. Improving latent factor models via personalized feature projection for one class recommendation. In Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, Melbourne, Australia, pp. 821–830, 2015.
Publicado
28/11/2022
Como Citar
GÔLO, Marcos P. S.; MORAES, Leonardo G.; GOULARTE, Rudinei; MARCACINI, Ricardo M..
One-Class Recommendation through Unsupervised Graph Neural Networks for Link Prediction. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 10. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 146-153.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2022.227810.
