The Impact of Representation Learning on Unsupervised Graph Neural Networks for One-Class Recommendation
DOI:
https://doi.org/10.5753/jidm.2024.3317Keywords:
One-Class Learning, Recommender Systems, Graph Neural Networks, Link Prediction, One-Class Explainability, Graph ExplainabilityAbstract
We present a Graph Neural Network (GNN) using link prediction for One-class Recommendation. Traditional recommender systems require positive and negative interactions to recommend items to users, but negative interactions are scarce, making it challenging to cover the scope of non-recommendations. Our proposed approach explores One-Class Learning (OCL) to overcome this limitation by using only one class (positive interactions) to train and predict whether or not a new example belongs to the training class in enriched heterogeneous graphs. The paper also proposes an explainability model and performs a qualitative evaluation through the TSNE algorithm in the learned embeddings. The methods' analysis in a two-dimensional projection showed our enriched graph neural network proposal was the only one that could separate the representations of users and items. Moreover, the proposed explainability method showed the user nodes connected with the predicted item are the most important to recommend this item to another user. Another conclusion from the experiments is that the added nodes to enrich the graph also impact the recommendation.
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Alam, S., Sonbhadra, S. K., Agarwal, S., and Nagabhushan, P. (2020). One-class support vector classifiers: A survey. Knowledge-Based Systems, 196:1–19. DOI: https://doi.org/10.1016/j.knosys.2020.105754.
da Silva, A. C. M., Gôlo, M., and Marcacini, R. (2022). Unsupervised heterogeneous graph neural network for hit song prediction through one class learning. In Proceedings of the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), pages 202–209, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/kdmile.2022.227954.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4171–4186, Minnesota. Association for Computational Linguistics. DOI: 10.18653/v1/N19-1423.
do Carmo, P. and Marcacini, R. (2021). Embedding propagation over heterogeneous event networks for link prediction. In Proceedings of the International Conference on Big Data (Big Data), pages 4812–4821, online. IEEE. DOI: 10.1109/BigData52589.2021.9671645.
Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., and Herrera, F. (2018). Learning from imbalanced data sets, volume 11. Springer, Switzerland. DOI: https://doi.org/10.1007/978-3-319-98074-4.
Gôlo, M., Caravanti, M., Rossi, R., Rezende, S., Nogueira, B., and Marcacini, R. (2021a). 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, pages 197–204, Belo Horizonte, MG, Brazil. ACM. DOI: https://doi.org/10.1145/3470482.3479634.
Gôlo, M. P., Araújo, A. F., Rossi, R. G., and Marcacini, R. M. (2022). Detecting relevant app reviews for software evolution and maintenance through multimodal one-class learning. Information and Software Technology, 151:106998. DOI: https://doi.org/10.1016/j.infsof.2022.106998.
Gôlo, M. P. S., de Souza, M. C., Rossi, R. G., Rezende, S. O., Nogueira, B. M., and Marcacini, R. M. (2023). One-class learning for fake news detection through multimodal variational autoencoders. Engineering Applications of Artificial Intelligence, 122:106088. DOI: https://doi.org/10.1016/j.engappai.2023.106088.
Gôlo, M. P. S., Rossi, R. G., and Marcacini, R. M. (2021b). Learning to sense from events via semantic variational autoencoder. Plos One, 16(12):e0260701. DOI: https://doi.org/10.1371/journal.pone.0260701.
Gôlo, M., Moraes, L., Goularte, R., and Marcacini, R. (2022). One-class recommendation through unsupervised graph neural networks for link prediction. In Proceedings of the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), pages 146–153, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/kdmile.2022.227810.
He, M., Pan, W., and Ming, Z. (2022). Bar: Behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering. Information Sciences, 608:881–899. DOI: https://doi.org/10.1016/j.ins.2022.06.084.
He, R. and McAuley, J. (2016). Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the International Conference on World Wide Web, pages 507–517, Republic and Canton of Geneva, Switzerland. ACM. DOI: https://doi.org/10.1145/2872427.2883037.
Islam, K., Aridhi, S., and Smaīl-Tabbone, M. (2020). A comparative study of similarity-based and gnn-based link prediction approaches. In Graph Embedding and Mining Workshop, Ghent, Belgium. HAL. DOI: https://hal.science/hal-03473689/.
Khoali, M., Laaziz, Y., Tali, A., and Salaudeen, H. (2022). A survey of one class e-commerce recommendation system techniques. Electronics, 11(6):878. DOI: https://doi.org/10.3390/electronics11060878.
Li, J., Shang, J., and McAuley, J. (2022). Uctopic: Unsupervised contrastive learning for phrase representations and topic mining. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, pages 6159–6169. DOI: 10.18653/v1/2022.acl-long.426.
Li, S. (2019). Food.com recipes and interactions.
Li, X. and Chen, H. (2013). Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach. Decision Support Systems, 54(2):880–890. DOI: https://doi.org/10.1016/j.dss.2012.09.019.
Liu, J., Pan, W., and Ming, Z. (2020). Cofigan: Collaborative filtering by generative and discriminative training for one-class recommendation. Knowledge-Based Systems, 191:105255. DOI: https://doi.org/10.1016/j.knosys.2019.105255.
Otter, D., Medina, J., and Kalita, J. (2020). A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32(2):604–624. DOI: 10.1109/TNNLS.2020.2979670.
Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., and Yang, Q. (2008). One-class collaborative filtering. In Proceedings of the International Conference on Data Mining, pages 502–511, Pisa, Italy. IEEE. DOI: 10.1109/ICDM.2008.16.
Rana, A., D’Addio, R. M., Manzato, M. G., and Bridge, D. (2022). Extended recommendation-by-explanation. User Modeling and User-Adapted Interaction, 32(1):91–131. DOI: https://doi.org/10.1007/s11257-021-09317-4.
Raziperchikolaei, R. and Chung, Y.-j. (2022). One-class recommendation systems with the hinge pairwise distance loss and orthogonal representations. arXiv preprint arXiv:2208.14594. DOI: https://doi.org/10.48550/arXiv.2208.14594.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). ”why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the International Conference on Knowledge Discovery and Data Mining, pages 1135–1144, San Francisco, CA, USA. DOI: https://doi.org/10.1145/2939672.2939778.
Rossi, R. G., Lopes, A. A., and Rezende, S. O. (2014). A parameter-free label propagation algorithm using bipartite heterogeneous networks for text classification. In Proceedings of the Annual Symposium on Applied Computing, pages 79–84, Gyeongju Republic of Korea. ACM. DOI: https://doi.org/10.1145/2554850.2554901.
Ru, S., Zhang, B., Jie, Y., Zhang, C., Wei, L., and Gu, C. (2021). Graph neural networks for privacy-preserving recommendation with secure hardware. In Proceedings of the International Conference on Networking and Network Applications (NaNA), pages 395–400, Lijiang City, China. IEEE. DOI: 10.1109/NaNA53684.2021.00075.
Tax, D. and Duin, R. (2004). Support vector data description. Machine Learning, 54(1):45–66. DOI: https://doi.org/10.1023/B:MACH.0000008084.60811.49.
Tax, D. M. J. (2001). One-class classification: concept-learning in the absence of counter-examples. PhD thesis, Delft University of Technology.
Trawinski, B., Smetek, M., Telec, Z., and Lasota, T. (2012). Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. Applied Mathematics and Computer Science, 22(4):867–881. DOI: https://doi.org/10.2478/v10006-012-0064-z.
Van der Maaten, L. and Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(11):2579–2605. DOI: http://jmlr.org/papers/v9/vandermaaten08a.html.
Wan, M. and McAuley, J. (2018). One-class recommendation with asymmetric textual feedback. In Proceedings of the SIAM International Conference on Data Mining, pages 648–656. SIAM. DOI: https://doi.org/10.1137/1.9781611975321.73.
Wu, S., Sun, F., Zhang, W., Xie, X., and Cui, B. (2020a). Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR), 55(97):1–37. DOI: https://doi.org/10.1145/3535101.
Wu, W., Li, B., Luo, C., and Nejdl, W. (2021). Hashing-accelerated graph neural networks for link prediction. In Proceedings of the Web Conference, pages 2910–2920, Ljubljana Slovenia. ACM. DOI: https://doi.org/10.1145/3442381.3449884.
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y. (2020b). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1):4–24. DOI: 10.1109/TNNLS.2020.2978386.
Yan, A., He, Z., Li, J., Zhang, T., and McAuley, J. (2022). Personalized showcases: Generating multi-modal explanations for recommendations. arXiv preprint arXiv:2207.00422. DOI: https://doi.org/10.48550/arXiv.2207.00422.
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., and Le, Q. V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems, 32:5753–5763. DOI: ISBN: 9781713807933.
Yuan, H., Yu, H., Gui, S., and Ji, S. (2022). Explainability in graph neural networks: A taxonomic survey. Transactions on Pattern Analysis and Machine Intelligence. DOI: 10.1109/TPAMI.2022.3204236.
Zhang, M. and Chen, Y. (2018). Link prediction based on graph neural networks. Advances in Neural Information Processing Systems, 31:11. DOI: ISBN: 9781510884472.
Zhao, T., McAuley, J., and King, I. (2015). Improving latent factor models via personalized feature projection for one class recommendation. In Proceedings of the International Conference on Information and Knowledge Management, pages 821–830, Melbourne, Australia. ACM. DOI: https://doi.org/10.1145/2806416.2806511.
