Position-Aware Neural Graph Collaborative Filtering to Resolve the Sparsity Problem in Recommender Systems

Shahla Havaas; alireza abdullahpouri; Nasser Yazdani

doi:10.21271/ZJPAS.37.5.11

Authors

Shahla Havaas Department of Computer Engineering, University of Kurdistan ,Kurdistan , Iran
alireza abdullahpouri Department of Computer Engineering, University of Kurdistan ,Kurdistan , Iran
Nasser Yazdani Department of Electrical and Computer Engineering, University of Tehran, Tehran , Iran

DOI:

https://doi.org/10.21271/ZJPAS.37.5.11

Keywords:

Recommender Systems, Sparsity Problem, GNN

Abstract

Users’ experience on the internet is positively influenced by recommendation systems, which help them in navigating through a lot of information available to them. The accuracy and relevance of recommendations can be improved only if advanced techniques like GNNs are used due to the increasing complexity associated with user-item interactions. Detailed patterns in preferences and behavior are captured by GNNs which model relationships between users and items. To this end, we present a Position-Aware Neural Graph Collaborative Filtering (PA-NGCF) model with two key differences from previous graph neural network-based recommender systems. In terms of our primary contribution, we have come up with a new way of creating node embeddings that make use of limited rating information from users to adequately capture user-item interactions. Furthermore, during message passing phase in our model, the positional information about nodes within the graph structure is explicitly included enabling richer understanding about the role and significance of each node involved in recommendation process. Two real-world datasets were used for extensive experiments to show how effective our approach was. Our findings indicate that PA-NGCF method effectively reduces the rating prediction error within recommendation systems. This error has been evaluated using the MAE and RMSE metrics. Our findings highlight the potential of the proposed method in increasing the quality of recommendations and addressing cold start problems in sparse datasets, as the reduction in prediction error leads to more accurate ranking of items adapted to user preferences.

References

Afoudi, Y., Lazaar, M. & Hmaidi, S. 2023. An enhanced recommender system based on heterogeneous graph link prediction. Engineering Applications of Artificial Intelligence, 124, 106553.

Anwer, M. A., Qattan, G. A. & Ali, A. M. 2024. Ocular disease classification using different kinds of machine learning algorithms. Zanco Journal of Pure and Applied Sciences, 36, 25-34.

Hamilton, W., Ying, Z. & Leskovec, J. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, 30.

Hamsheen, E. S. & Flah, L. R. 2023. Improvement performance by using Machine learning algorithms for fake news detection. Zanco Journal of Pure and Applied Sciences, 35, 48-57.

He, X., Deng, K., Wang, X., Li, Y., Zhang, Y. & Wang, M. Lightgcn: Simplifying and powering graph convolution network for recommendation. Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020. 639-648.

Hu, Z., Dong, Y., Wang, K. & Sun, Y. Heterogeneous graph transformer. Proceedings of the web conference 2020, 2020. 2704-2710.

Koren, Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008. 426-434.

Koren, Y., Bell, R. & Volinsky, C. 2009. Matrix factorization techniques for recommender systems. Computer, 42, 30-37.

Koren, Y., Rendle, S. & Bell, R. 2021. Advances in collaborative filtering. Recommender systems handbook, 91-142.

Lee, D. & Seung, H. S. 2000. Algorithms for non-negative matrix factorization. Advances in neural information processing systems, 13.

Liang, S., Sun, R., Lee, J. D. & Srikant, R. 2018. Adding one neuron can eliminate all bad local minima. Advances in Neural Information Processing Systems, 31.

Lin, Z., Tian, C., Hou, Y. & Zhao, W. X. Improving graph collaborative filtering with neighborhood-enriched contrastive learning. Proceedings of the ACM web conference 2022, 2022. 2320-2329.

Wang, X., He, X., Wang, M., Feng, F. & Chua, T.-S. Neural graph collaborative filtering. Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, 2019. 165-174.

Wu, B., Wen, W., Hao, Z. & Cai, R. 2020. Multi-context aware user–item embedding for recommendation. Neural Networks, 124, 86-94.

Wu, S., Sun, F., Zhang, W., Xie, X. & Cui, B. 2022. Graph neural networks in recommender systems: a survey. ACM Computing Surveys, 55, 1-37.

Xiao, J., Ye, H., He, X., Zhang, H., Wu, F. & Chua, T.-S. 2017. Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv preprint arXiv:1708.04617.

Xu, B., Wang, N., Chen, T. & Li, M. 2015. Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853.

Yang, D., Chen, L., Liang, J., Xiao, Y. & Wang, W. Social tag embedding for the recommendation with sparse user-item interactions. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018. IEEE, 127-134.

Yang, X., Yan, M., Pan, S., Ye, X. & Fan, D. Simple and efficient heterogeneous graph neural network. Proceedings of the AAAI conference on artificial intelligence, 2023. 10816-10824.

Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L. & Leskovec, J. Graph convolutional neural networks for web-scale recommender systems. Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 2018. 974-983.

Yu, J., Xia, X., Chen, T., Cui, L., Hung, N. Q. V. & Yin, H. 2023. XSimGCL: Towards extremely simple graph contrastive learning for recommendation. IEEE Transactions on Knowledge and Data Engineering, 36, 913-926.

Yu, J., Yin, H., Xia, X., Chen, T., Cui, L. & Nguyen, Q. V. H. Are graph augmentations necessary? simple graph contrastive learning for recommendation. Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, 2022. 1294-1303.

Zhu, Q., Yang, C., Xu, Y., Wang, H., Zhang, C. & Han, J. 2021. Transfer learning of graph neural networks with ego-graph information maximization. Advances in Neural Information Processing Systems, 34, 1766-1779.