A Comparative Study of GNN Architectures and Self-Supervised Objectives for Embedding Extraction in Recommender Systems
Resumo
This study systematically evaluates item embeddings generated through 16 combinations of Graph Neural Network architectures and self-supervised objectives for recommender systems. Using datasets from two distinct domains, we analyze these structural representations both in isolation and as initialization for sequential modeling. Findings reveal that embedding effectiveness is highly sensitive to domain density. Ultimately, results demonstrate that hybridizing graph-based topological information with sequential patterns significantly enhances recommendation performance and catalog coverage, reinforcing the robust synergy between structural and temporal dynamics.
Referências
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A nextgeneration hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2623–2631.
Belkin, M. and Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373–1396.
Cooley, R., Mobasher, B., and Srivastava, J. (1999). Data preparation for mining world wide web browsing patterns. Knowledge and information systems, 1:5–32.
Covington, P., Adams, J., and Sargin, E. (2016). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems, pages 191–198.
Domingues, M. A., de Moura, E. S., Marinho, L. B., and da Silva, A. (2022). Benchmarking session-based and session-aware recommender systems for jusbrasil. In Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia), pages 145–148. SBC.
Domingues, M. A., de Moura, E. S., Marinho, L. B., and da Silva, A. (2025). A large scale benchmark for session-based recommendations on the legal domain. Artificial Intelligence and Law, 33(1):43–78.
Hagen, A. N. (2015). The playlist experience: Personal playlists in music streaming services. Popular Music and Society, 38(5):625–645.
Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in neural information processing systems, 30.
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., and Wang, M. (2020). Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pages 639–648.
Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015). Session-based recommendations with recurrent neural networks.
Kang, W.-C. and McAuley, J. (2018). Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM), pages 197–206. IEEE.
Kipf, T. N. and Welling, M. (2016a). Semi-supervised classification with graph convolutional networks.
Kipf, T. N. and Welling, M. (2016b). Variational graph auto-encoders.
Latifi, S., Mauro, N., and Jannach, D. (2021). Session-aware recommendation: A surprising quest for the state-of-the-art. Information Sciences, 573:291–315.
Ludewig, M. and Jannach, D. (2018). Evaluation of session-based recommendation algorithms. User Modeling and User-Adapted Interaction, 28:331–390.
Peintner, A., Moscati, M., Parada-Cabaleiro, E., Schedl, M., and Zangerle, E. (2025). Unsupervised graph embeddings for session-based recommendation with item features.
Santana, I. A. P., Pinhelli, F., Donini, J., Catharin, L., Mangolin, R. B., Feltrim, V. D., Domingues, M. A., et al. (2020). Music4all: A new music database and its applications. In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pages 399–404. IEEE.
Shehzad, F. and Jannach, D. (2024). Performance comparison of session-based recommendation algorithms based on gnns. In European Conference on Information Retrieval, pages 115–131. Springer.
Shin, Y.-M., Tran, C., Shin, W.-Y., and Cao, X. (2023). Edgeless-gnn: Unsupervised representation learning for edgeless nodes. IEEE Transactions on Emerging Topics in Computing, 12(1):150–162.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks.
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D. (2018). Deep graph infomax.
Wan, Z., Liu, X., Wang, B., Qiu, J., Li, B., Guo, T., Chen, G., and Wang, Y. (2023). Spatio-temporal contrastive learning-enhanced gnns for session-based recommendation. ACM Transactions on Information Systems, 42(2):1–26.
Wang, F., Gao, X., Chen, Z., and Lyu, L. (2023). Contrastive multi-level graph neural networks for session-based recommendation. IEEE Transactions on Multimedia, 25:9278–9289.
Wang, S., Cao, L., Wang, Y., Sheng, Q. Z., Orgun, M. A., and Lian, D. (2021). A survey on session-based recommender systems. ACM Computing Surveys, 54(7):1–38.
Wang, S., Pasi, G., Hu, L., and Cao, L. (2020a). The era of intelligent recommendation: Editorial on intelligent recommendation with advanced ai and learning. IEEE Intell. Syst., 35(5):3–6.
Wang, Z., Wei, W., Cong, G., Li, X.-L., Mao, X.-L., and Qiu, M. (2020b). Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pages 169–178.
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., and Tan, T. (2019). Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 346–353.
Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2018). How powerful are graph neural networks? Yin, Q., Fang, H., Sun, Z., and Ong, Y.-S. (2023). Understanding diversity in session-based recommendation. ACM Transactions on Information Systems, 42(1):1–34.
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J. (2018). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 974–983.
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y. (2020). Graph contrastive learning with augmentations. Advances in neural information processing systems, 33:5812–5823.
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L. (2020). Deep graph contrastive representation learning.
Zykov, R., Noskov, A., and Anokhin, A. (2022). Retailrocket recommender system dataset.
Belkin, M. and Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6):1373–1396.
Cooley, R., Mobasher, B., and Srivastava, J. (1999). Data preparation for mining world wide web browsing patterns. Knowledge and information systems, 1:5–32.
Covington, P., Adams, J., and Sargin, E. (2016). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems, pages 191–198.
Domingues, M. A., de Moura, E. S., Marinho, L. B., and da Silva, A. (2022). Benchmarking session-based and session-aware recommender systems for jusbrasil. In Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia), pages 145–148. SBC.
Domingues, M. A., de Moura, E. S., Marinho, L. B., and da Silva, A. (2025). A large scale benchmark for session-based recommendations on the legal domain. Artificial Intelligence and Law, 33(1):43–78.
Hagen, A. N. (2015). The playlist experience: Personal playlists in music streaming services. Popular Music and Society, 38(5):625–645.
Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in neural information processing systems, 30.
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., and Wang, M. (2020). Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pages 639–648.
Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015). Session-based recommendations with recurrent neural networks.
Kang, W.-C. and McAuley, J. (2018). Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM), pages 197–206. IEEE.
Kipf, T. N. and Welling, M. (2016a). Semi-supervised classification with graph convolutional networks.
Kipf, T. N. and Welling, M. (2016b). Variational graph auto-encoders.
Latifi, S., Mauro, N., and Jannach, D. (2021). Session-aware recommendation: A surprising quest for the state-of-the-art. Information Sciences, 573:291–315.
Ludewig, M. and Jannach, D. (2018). Evaluation of session-based recommendation algorithms. User Modeling and User-Adapted Interaction, 28:331–390.
Peintner, A., Moscati, M., Parada-Cabaleiro, E., Schedl, M., and Zangerle, E. (2025). Unsupervised graph embeddings for session-based recommendation with item features.
Santana, I. A. P., Pinhelli, F., Donini, J., Catharin, L., Mangolin, R. B., Feltrim, V. D., Domingues, M. A., et al. (2020). Music4all: A new music database and its applications. In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), pages 399–404. IEEE.
Shehzad, F. and Jannach, D. (2024). Performance comparison of session-based recommendation algorithms based on gnns. In European Conference on Information Retrieval, pages 115–131. Springer.
Shin, Y.-M., Tran, C., Shin, W.-Y., and Cao, X. (2023). Edgeless-gnn: Unsupervised representation learning for edgeless nodes. IEEE Transactions on Emerging Topics in Computing, 12(1):150–162.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks.
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D. (2018). Deep graph infomax.
Wan, Z., Liu, X., Wang, B., Qiu, J., Li, B., Guo, T., Chen, G., and Wang, Y. (2023). Spatio-temporal contrastive learning-enhanced gnns for session-based recommendation. ACM Transactions on Information Systems, 42(2):1–26.
Wang, F., Gao, X., Chen, Z., and Lyu, L. (2023). Contrastive multi-level graph neural networks for session-based recommendation. IEEE Transactions on Multimedia, 25:9278–9289.
Wang, S., Cao, L., Wang, Y., Sheng, Q. Z., Orgun, M. A., and Lian, D. (2021). A survey on session-based recommender systems. ACM Computing Surveys, 54(7):1–38.
Wang, S., Pasi, G., Hu, L., and Cao, L. (2020a). The era of intelligent recommendation: Editorial on intelligent recommendation with advanced ai and learning. IEEE Intell. Syst., 35(5):3–6.
Wang, Z., Wei, W., Cong, G., Li, X.-L., Mao, X.-L., and Qiu, M. (2020b). Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pages 169–178.
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., and Tan, T. (2019). Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 346–353.
Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2018). How powerful are graph neural networks? Yin, Q., Fang, H., Sun, Z., and Ong, Y.-S. (2023). Understanding diversity in session-based recommendation. ACM Transactions on Information Systems, 42(1):1–34.
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J. (2018). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 974–983.
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y. (2020). Graph contrastive learning with augmentations. Advances in neural information processing systems, 33:5812–5823.
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L. (2020). Deep graph contrastive representation learning.
Zykov, R., Noskov, A., and Anokhin, A. (2022). Retailrocket recommender system dataset.
Publicado
19/07/2026
Como Citar
LEME, Matheus H. C.; SANTOS, Giovani O.; SIMM, Vinicius S.; TANNO, Douglas R.; DOMINGUES, Marcos A..
A Comparative Study of GNN Architectures and Self-Supervised Objectives for Embedding Extraction in Recommender Systems. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 53. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 13-24.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2026.21074.
