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A Novel High-Order Cluster-GCN-Based Approach for Service Recommendation

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Web Services – ICWS 2021 (ICWS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12994))

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Abstract

When exploring high-order neighbors for embedding learning, data sparsity problems in service recommendation system can be compensated via Graph Convolutional Network (GCN). However, the performance of GCN will deteriorate when stacking more layers, namely, over-smoothing problem. Though LightGCN and LR-GCN can alleviate over-smoothing and achieve state-of-the-art performance, all users with dissimilar preferences become similar and the services become homogeneous, introducing noise information in exploration high-order graph convolution. Thus, we argue that the loss of uniqueness of all nodes is the cause of over-smoothing problems in high-order graph convolution. To solve the above problems, we propose to use graph clustering algorithm to cluster user-service graph. Moreover, this node enhancement technique in our model can further facilitate systems to learn more information from nearby neighbors. The experimental results confirm that the proposed algorithm outperforms most baseline algorithms, achieving state-of-the-art performance.

M. Luo and P. Chen—Contribute equally to this work and thus are co-first authors.

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References

  1. Breese, J.S.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Madison, WI (1998)

    Google Scholar 

  2. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  3. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)

    Google Scholar 

  4. Chen, J., Zhang, H., He, X., Nie, L., Liu, W., Chua, T.S.: Attentive collaborative filtering: multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–344 (2017)

    Google Scholar 

  5. He, X., He, Z., Song, J., Liu, Z., Jiang, Y.G., Chua, T.S.: Nais: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354–2366 (2018)

    Article  Google Scholar 

  6. Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering, pp. 165–174 (2019)

    Google Scholar 

  7. Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 1, pp. 27–34 (2020)

    Google Scholar 

  8. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: 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, pp. 639–648 (2020)

    Google Scholar 

  9. Liu, F., Cheng, Z., Zhu, L., Gao, Z., Nie, L.: Interest-aware message-passing gcn for recommendation. arXiv preprint arXiv:2102.10044 (2021)

  10. Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)

    Article  MathSciNet  Google Scholar 

  11. Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. Adv. Neural. Inf. Process. Syst. 20, 1257–1264 (2007)

    Google Scholar 

  12. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434 (2008)

    Google Scholar 

  13. Yang, J.H., Chen, C.M., Wang, C.J., Tsai, M.F.: Hop-rec: high-order proximity for implicit recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 140–144 (2018)

    Google Scholar 

  14. Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216 (2017)

  15. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  16. Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)

  17. Berg, R.v.d., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)

  18. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983 (2018)

    Google Scholar 

  19. Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, PMLR, pp. 6861–6871 (2019)

    Google Scholar 

  20. Chiang, W.L., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.J.: Cluster-gcn: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 257–266 (2019)

    Google Scholar 

  21. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)

  22. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)

    Google Scholar 

  23. Liang, D., Charlin, L., Mcinerney, J., Blei, D.M.: Modeling user exposure in recommendation. In: Proceedings of the 25th International Conference (2016)

    Google Scholar 

  24. He, R., Mcauley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the International World Wide Web Conferences Steering Committee (2016)

    Google Scholar 

  25. He, R., McAuley, J.: Vbpr: visual bayesian personalized ranking from implicit feedback. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)

    Google Scholar 

  26. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, pp. 249–256 (2010)

    Google Scholar 

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Luo, M. et al. (2022). A Novel High-Order Cluster-GCN-Based Approach for Service Recommendation. In: Xu, C., Xia, Y., Zhang, Y., Zhang, LJ. (eds) Web Services – ICWS 2021. ICWS 2021. Lecture Notes in Computer Science(), vol 12994. Springer, Cham. https://doi.org/10.1007/978-3-030-96140-4_3

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  • DOI: https://doi.org/10.1007/978-3-030-96140-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96139-8

  • Online ISBN: 978-3-030-96140-4

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