Abstract
In light of the remarkable capacity of graph convolutional network (GCN) in representation learning, researchers have incorporated it into collaborative filtering recommendation systems to capture high-order collaborative signals. However, existing GCN-based collaborative filtering models still exhibit three deficiencies: the failure to consider differences between users’ activity and preferences for items’ popularity, the low-order feature information of users and items has been inadequately employed, and neglecting the correlated relationships among isomorphic nodes. To address these shortcomings, this paper proposes a degree-aware embedding-based multi-correlated graph convolutional collaborative filtering (Da-MCGCF). Firstly, Da-MCGCF combines users’ activity and preferences for items’ popularity to perform neighborhood aggregation in the user-item bipartite graph, thereby generating more precise representations of users and items. Secondly, Da-MCGCF employs a low-order feature fusion strategy to integrate low-order features into the process of mining high-order features, which enhances feature representation capabilities, and enables the exploration of deeper relationships. Furthermore, we construct two isomorphic graphs by employing an adaptive approach to explore correlated relationships at the isomorphic level between users and items. Subsequently, we aggregate the features of isomorphic users and items separately to complement their representations. Finally, we conducted extensive experiments on four public datasets, thereby validating the effectiveness of our proposed model.
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Data availability
Ciao dataset is available at http://www.ciao.co.uk, Yelp dataset is available at https://www.yelp.com/dataset, ML-1 M dataset is available at https://github.com/familyld/DeepCF and Gowalla dataset is available at https://snap.stanford.edu/data/loc-gowalla.html.
Code availability
The source code of Da-MCGCF will be published at https://github.com/xiaoma012/Da-MCGCF after paper acceptance for publication.
References
Chen H, Li Z, Hu W (2016) An improved collaborative recommendation algorithm based on optimized user similarity. J Supercomput 72:2565–2578. https://doi.org/10.1007/s11227-015-1518-5
Rahim A, Durrani MY, Gillani S, Ali Z, Hasan NU, Kim M (2022) An efficient recommender system algorithm using trust data. J Supercomput, pp 1–21. https://doi.org/10.1007/s11227-021-03991-2
Ricci F, Rokach L, Shapira B (2020) Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp 1–35. https://doi.org/10.1007/978-0-387-85820-3_1
Desarkar MS, Saxena R, Sarkar S (2012) Preference relation based matrix factorization for recommender systems. In: User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16–20, 2012. Proceedings 20, pp 63–75. https://doi.org/10.1007/978-3-642-31454-4_6
Fang J, Grunberg D, Lui S, Wang Y (2017) Development of a music recommendation system for motivating exercise. In: 2017 International Conference on Orange Technologies (ICOT), pp 83–86. https://doi.org/10.1109/ICOT.2017.8336094
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp 285–295. https://doi.org/10.1145/371920.372071
Pujahari A, Sisodia DS (2021) Preference relation based collaborative filtering with graph aggregation for group recommender system. Appl Intell 51:658–672. https://doi.org/10.1007/s10489-020-01848-4
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37. https://doi.org/10.1109/MC.2009.263
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp 173–182. https://doi.org/10.1109/MC.2009.263
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint https://doi.org/10.48550/arXiv.1609.02907
Lee Y-C, Son J, Kim T, Park D, Kim S-W (2021) Exploiting uninteresting items for effective graph-based one-class collaborative filtering. J Supercomput 77:6832–6851. https://doi.org/10.1007/s11227-020-03573-8
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, 29. https://doi.org/10.48550/arXiv.1609.02907
Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, 30. https://doi.org/10.48550/arXiv.1706.02216
Ma Y, Wang S, Aggarwal CC, Yin D, Tang J (2019) Multi-dimensional graph convolutional networks. In: Proceedings of the 2019 Siam International Conference on Data Mining, pp 657–665. https://doi.org/10.48550/arXiv.1808.06099
Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp 6861–6871. https://doi.org/10.48550/arXiv.1902.07153
Li G, Muller M, Thabet A, Ghanem B (2019) Deepgcns: can gcns go as deep as cnns? In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 9267–9276. https://doi.org/10.48550/arXiv.1904.03751
Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 165–174 https://doi.org/10.1145/3331184.3331267
He X, Deng K, Wang X, Li Y, Zhang Y, 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, pp 639–648. https://doi.org/10.1145/3397271.3401063
Mao K, Zhu J, Xiao X, Lu B, Wang Z, He X (2021) Ultragcn: ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, pp 1253–1262. https://doi.org/10.1145/3459637.3482291
Li X, Guo R, Chen J, Hu Y, Qu M, Jiang B (2023) Effective hybrid graph and hypergraph convolution network for collaborative filtering. Neural Comput Appl 35(3):2633–2646. https://doi.org/10.1007/s00521-022-07735-y
Chen L, Wu L, Hong R, Zhang K, Wang M (2020) Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI Conference on Artificial Intelligence vol. 34, pp 27–34. https://doi.org/10.1609/aaai.v34i01.5330
Liu K, Xue F, He X, Guo D, Hong R (2022) Joint multi-grained popularity-aware graph convolution collaborative filtering for recommendation. IEEE Trans Comput Soc Syst. https://doi.org/10.1109/TCSS.2022.3151822
Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp 39–46. https://doi.org/10.1145/1864708.1864721
Steck H (2011) Item popularity and recommendation accuracy. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp 125–132. https://doi.org/10.1145/2043932.2043957
Li H, Liu J, Cao B, Tang M, Liu X, Li B (2017) Integrating tag, topic, co-occurrence, and popularity to recommend web apis for mashup creation. In: 2017 IEEE International Conference on Services Computing (SCC), pp 84–91. https://doi.org/10.1109/SCC.2017.19
Wang X, Jin H, Zhang A, He X, Xu T, Chua T-S (2020) Disentangled graph collaborative filtering. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1001–1010. https://doi.org/10.1145/3397271.3401137
Zheng L, Lu C-T, Jiang F, Zhang J, Yu PS (2018) Spectral collaborative filtering. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp 311–319. https://doi.org/10.1145/3240323.3240343
Xue G, Zhong M, Li J, Chen J, Zhai C, Kong R (2022) Dynamic network embedding survey. Neurocomputing 472:212–223. https://doi.org/10.1016/j.neucom.2021.03.138
Berg R, Kipf TN, Welling M (2017) Graph convolutional matrix completion. arXiv preprint https://doi.org/10.48550/arXiv.1706.02263
Sun J, Zhang Y, Ma C, Coates M, Guo H, Tang R, He X (2019) Multi-graph convolution collaborative filtering. In: 2019 IEEE International Conference on Data Mining (ICDM), pp 1306–1311. https://doi.org/10.1109/ICDM.2019.00165
Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: Proceedings of the 37th International Conference on Machine Learning. ICML’20, pp 1725–1735. https://doi.org/10.48550/arXiv.2007.02133
Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 32. https://doi.org/10.1609/aaai.v32i1.11604
Guo Z, Wang C, Li Z, Li J, Li G (2022) Joint locality preservation and adaptive combination for graph collaborative filtering. In: Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022, Virtual Event, April 11–14, 2022, Proceedings, Part II, pp 183–198. https://doi.org/10.1007/978-3-031-00126-0_12
Li G, Guo Z, Li J, Wang C (2022) Mdgcf: multi-dependency graph collaborative filtering with neighborhood-and homogeneous-level dependencies. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, pp 1094–1103. https://doi.org/10.1145/3511808.3557390
Hoecker A, Kartvelishvili V (1996) Svd approach to data unfolding. Nucl Instrum Methods Phys Res Sect A 372(3):469–481. https://doi.org/10.1016/0168-9002(95)01478-0
Liu M, Li J, Liu K, Wang C, Peng P, Li G, Cheng Y, Jia G, Xie W (2022) Graph-icf: item-based collaborative filtering based on graph neural network. Knowl-Based Syst 251:109208. https://doi.org/10.1016/j.knosys.2022.109208
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) Bpr: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618
Buckland M, Gey F (1994) The relationship between recall and precision. J Am Soc Inf Sci 45(1):12–19
He X, Chen T, Kan M-Y, Chen X (2015) Trirank: Review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp 1661–1670. https://doi.org/10.1145/2806416.2806504
Kong T, Kim T, Jeon J, Choi J, Lee Y-C, Park N, Kim S-W (2022) Linear, or non-linear, that is the question! In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp 517–525. https://doi.org/10.1145/3488560.3498501
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This work was supported by the Science Fund for Outstanding Youth of Xinjiang Uygur Autonomous Region under Grant No.2021D01E14.
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Chao Ma designed and performed the experiments, formal analysis, and writing. Jiwei Qin reviewing and editing manuscript. Tao Wang and Aohua Gao give advice. All authors reviewed the Manuscript.
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Ma, C., Qin, J., Wang, T. et al. Degree-aware embedding-based multi-correlated graph convolutional collaborative filtering. J Supercomput 80, 25911–25932 (2024). https://doi.org/10.1007/s11227-024-06354-9
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DOI: https://doi.org/10.1007/s11227-024-06354-9