Applying Graph Convolution Networks to Recommender Systems based on graph topology

Yıl 2022, , 205 - 211, 28.06.2022

Alper Özcan

https://doi.org/10.24012/dumf.1081137

Öz

The recommender systems are widely used in online applications to suggest products to the potential users. The main aim of recommender system is to produce meaningful recommendation to a potential user by monitoring user’s purchasing habits, history, and useful information. Recently, graph representation learning methods based on node embedding have drawn attention in Recommender systems such as Graph Convolutional Networks (GCNs) that is powerful method for collaborative filtering. The GCN performs neighborhood aggregation mechanism to extract high level representation for both user and items. In this paper, we propose a recommendation algorithm based on node similarity convolutional matrices with topological property in GCNs where the linkage measure is illustrated as a bipartite graph. The experiments indicate the necessity of capturing user–item graph structure in recommendation. The experimental results show that node similarity-based convolution matrices and GCN-based embeddings significantly improve the prediction accuracy in recommender systems compared to state-of-art approaches.

Anahtar Kelimeler

Recommender systems, graph convolution network, deep learning, graph modelling

Kaynakça

• [1] M. Chui, "Artificial intelligence the next digital frontier?", McKinsey and Company Global Institute, 47:3–6, 2017.
• [2] H.Cheng, L. Koc, J. Harmsen, H. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai and M. Ispir, “Wide & Deep Learning for Recommender Systems”, In Proceedings of the 1st workshop on deep learning for recommender systems, pp. 7–10, 2016.
• [3] H. Guo, R. Tang, Y. Ye, Z. Li and X. He, "DeepFM: a factorization-machine based neural network for CTR prediction”, In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1725-1731, 2017.
• [4] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. Chua, “Neural collaborative filtering”, In Proceedings of the 26th international conference on world wide web, pp. 173-182, 2017.
• [5] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems”, Computer, 42(8), pp. 30-37, 2009.
• [6] S. Rendle, “Factorization machines”, In 2010 IEEE International conference on data mining IEEE, pp. 995-1000, 2010.
• [7] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “BPR: Bayesian personalized ranking from implicit feedback”, In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 452-461, 2009.
• [8] X. He, K. Deng, X. Wang, Y.N. Li, Y. Zhang, and M. Wang, “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.
• [9] X. Wang, X. He, M. Wang, F. Feng, and T. Chua, “Neural graph collaborative filtering”, In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, pp. 165-174, 2019.
• [10] R. Ying, R. He, K. Chen, P. Eksombatchai, W.L. Hamilton, and J. Leskovec, “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.
• [11] F. Monti, M. Bronstein, and X. Bresson, “Geometric matrix completion with recurrent multi-graph neural networks”, In NIPS, pp. 3697-3707, 2017.
• [12] H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo, “Knowledge graph convolutional networks for recommender systems”, In the world wide web conference, ACM, pp. 3307-3313, 2019.
• [13] X. Wang, X. He, Y. Cao, M. Liu, and T. Chua, “KGAT: Knowledge Graph Attention Network for Recommendation”, In KDD, pp. 950-958, 2019.
• [14] S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-based recommendation with graph neural networks”, In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. Pp. 346-353, 2019.
• [15] C. Gao, Y. Zheng, N. Li, Y. Qin, J. Piao, Y. Quan, J. Chang, D. Jin, X. He, and Y. Li, “Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions”, arXiv, 2021.
• [16] M. J. Pazzani and D. Billsus, “Content-based recommendation systems. The adaptive web: methods and strategies of web personalization”, Springer-Verlag, Berlin, Heidelberg, 325–341, 2007.
• [17] R. J. Mooney and L. Roy, “Content-based book recommending using learning for text categorization”, In Proceedings of the Fifth ACM Conference on Digital Libraries, DL ’00, pages 195–204, ACM, 2000.
• [18] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering”, IEEE Internet computing, 7(1):76–80, 2003.
• [19] J. Leskovec, A. Rajaraman, and J. D. Ullman, “Recommendation Systems”, Cambridge University Press, 2 edition, 292–324, 2014.
• [20] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems”, Computer, 42(8):30–37, 2009.
• [21] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Analysis of recommendation algorithms for e-commerce”, In Proceedings of the 2Nd ACM Conference on Electronic Commerce, EC ’00, pages 158–167. ACM, 2000.
• [22] B. Shams and S. Haratizadeh, “Graph-based collaborative ranking”, Expert Syst. Appl., 67(C):59– 70, 2017.
• [23] D. Liben-Nowell and J. Kleinberg, “The link-prediction problem for social networks”, Journal of The American Society For Information Science and Technology, 58(7):1019–1031, 2007.
• [24] L. Zhang, M. Zhao, and D. Zhao, “Bipartite graph link prediction method with homogeneous nodes similarity for music recommendation”, Multimedia Tools and Applications, pages 1–19, 2020.
• [25] X. Li and H. Chen, “Recommendation as link prediction in bipartite graphs: A graph kernelbased machine learning approach”, Decision Support Systems, 54(2):880–890, 2013.
• [26] L. Zhang, M. Zhao, and D. Zhao, “Bipartite graph link prediction method with homogeneous nodes similarity for music recommendation”, Multimedia Tools and Applications, pages 1–19, 2020.
• [27] R. N. Lichtenwalter, J. T. Lussier, and N. V. Chawla, “New perspectives and methods in link prediction”, In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD’10, pages 243–252. ACM, 2010.
• [28] B. Shams and S. Haratizadeh, “Graph-based collaborative ranking”, Expert Syst. Appl., 67(C):59– 70, 2017.
• [29] T. J. Lakshmi and S. D. Bhavani, “Temporal probabilistic measure for link prediction in collaborative networks”, Applied Intelligence, 47(1):83–95, Jul 2017.
• [30] R. Qiu, J. Li, Z. Huang, and H. Yin, “Rethinking the item order in session-based recommendation with graph neural networks”, In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 579-588, 2019.
• [31] J. Zhang, X. Shi, S. Zhao, and I. King, “STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender System”, IJCAI, 2019.
• [32] Y. Zheng, C. Gao, L. Chen, D. Jin, and Y. Li, “DGCN: Diversified Recommendation with Graph Convolutional Networks”, In Proceedings of the Web Conference, 401-412, 2021.
• [33] D. Liben-Nowell, J.M. Kleinberg, “The link-prediction problem for social networks”, Journal of the American Society for Information Science and Technology , 58, 1019-1031, 2007.
• [34] T. Zhou, L.-Y. Lu, Y.-C. Zhang, “Predicting Missing Links via Local Information”, arXiv: 0901.0553, 2009.
• [35] Z. Huang, D. D. Zeng, H. Chen, “Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems”, Management science. Vol. 53, No. 7, pp. 1146–1164 2007.
• [36] J. Ben Schafer, Joseph A. Konstan, and John Riedl. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1):115–153, 2001.
• [37] David K Hammond, Pierre Vandergheynst, and Rémi Gribonval. 2011. Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis 30, 2 (2011), 129–150.
• [38] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32, 1 (2020), 4–24.