A collaborative filtering model based on heterogeneous graph neural network
YANG Bo1,2,3, QIU Lei3, WU Shu4
1. College of Information, Beijing Forestry University, Beijing 100083, China; 2. Forestry Intelligent Information Processing Engineering Technology Research Center, National Forestry and Grassland Administration, Beijing 100083, China; 3. College of Information, North China University of Technology, Beijing 100144, China; 4. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Abstract：[Objective] Collaborative filtering algorithms are widely used in various recommendation systems and can be used to recommend information of interest to users similar to the user based on historical data. Recently, collaborative filtering algorithms based on graph neural networks have become one of the hot research topics. A collaborative filtering model based on a graph structure usually encodes the interaction between users and information items as a two-part diagram, and high-order connectivity modeling of the bipartite graph can be used to capture the hidden relationship between the user and the item. However, this bipartite graph model does not explicitly obtain the similarity relationship between users and between items. Additionally, the bipartite graph sparsity causes high-order connectivity dependence problems in the model. [Methods] Herein, a collaborative filtering model is proposed based on a heterogeneous graph convolutional neural network that explicitly encodes the similarities between users and that between items into the graph structure so that the interaction relationships between users and between items are modeled as a heterogeneous graph. The heterogeneous graph structure allows the similarities between users and between items to be directly captured, reducing the need for high-order connectivity and alleviating the bipartite graph sparsity problem. [Results] We conducted experiments on four typical datasets and compared the results using four typical methods. The results showed that our model achieved better experimental results than the traditional collaborative filtering models and existing graph neural network models. Moreover, based on the different types of edges, different similarity methods, and different similarity thresholds, our model obtained better experimental results. [Conclusions] Our model explicitly encodes the similarities between users and between items into the heterogeneous graph structure as edges so that the model can directly learn these similarities during training to get the embedded information of users and items. The proposed model alleviates the sparsity and high-order connectivity modeling problems of bipartite graphs. The collaborative filtering model based on heterogeneous graph neural networks can also fully capture the interaction relationships between users and items through low-order connectivity in the graph.
杨波, 邱雷, 吴书. 异质图神经网络协同过滤模型[J]. 清华大学学报（自然科学版）, 2023, 63(9): 1339-1349.
YANG Bo, QIU Lei, WU Shu. A collaborative filtering model based on heterogeneous graph neural network. Journal of Tsinghua University(Science and Technology), 2023, 63(9): 1339-1349.
[1] KIPF T N, WELLING M. Semi-s upervised classification with graph convolutional networks[C]//Proceedings of the 5th International Conference on Learning Representations. Toulon, France: OpenReview.net, 2017. [2] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37. [3] HE X N, LIAO L Z, ZHANG H W, et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web. Perth, Australia: International World Wide Web Conferences Steering Committee, 2017: 173-182. [4] WANG X, HE X N, WANG M, et al. Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Paris, France: Association for Computing Machinery, 2019: 165-174. [5] QASEM S N, MOHAMMADZADEH A. A deep learned type-2 fuzzy neural network: Singular value decomposition approach[J]. Applied Soft Computing, 2021, 105: 107244. [6] NGUYEN J, ZHU M. Content-boosted matrix factorization techniques for recommender systems[J]. Statistical Analysis and Data Mining, 2013, 6(4): 286-301. [7] WU L, SUN P J, HONG R C, et al. SocialGCN: An efficient graph convolutional network based model for social recommendation[Z/OL]. arXiv: 1811.02815, 2018. [8] PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk: Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2014: 701-710. [9] YANG Y, CHEN H R, SHAO J M. Triplet enhanced autoencoder: Model-free discriminative network embedding[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China: IJCAI.org, 2019: 5363-5369. [10] LI S W, CHEN R Y, SUN C P, et al. Region-aware neural graph collaborative filtering for personalized recommendation[J]. International Journal of Digital Earth, 2022, 15(1): 1446-1462. [11] XIA J F, LI D S, GU H S, et al. Incremental graph convolutional network for collaborative filtering[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Queensland, Australia: Association for Computing Machinery, 2021: 2170-2179. [12] SHAH S M A, GE H W, HAIDER S A, et al. A quantum spatial graph convolutional network for text classification[J]. Computer Systems Science and Engineering, 2021, 36(2): 369-382. [13] VAN DEN BERG R, KIPF T N, WELLING M. Graph convolutional matrix completion[Z/OL]. arXiv: 1706.02263, 2017. [14] YANG J H, CHEN C M, WANG C J, et al. HOP-rec: High-order proximity for implicit recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems. Vancouver, Canada: Association for Computing Machinery, 2018: 140-144. [15] LI Q M, HAN Z C, WU X M. Deeper insights into graph convolutional networks for semi-supervised learning[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, USA: AAAI, 2018: 3538-3545. [16] ZHAO J A, WANG X, SHI C, et al. Heterogeneous graph structure learning for graph neural networks[C/OL]//Proceedings of the 35th AAAI Conference on Artificial Intelligence. Held Virtually: AAAI, 2021: 4697-4705. [17] YANG T C, HU L M, SHI C, et al. HGAT: Heterogeneous graph attention networks for semi-supervised short text classification[J]. ACM Transactions on Information Systems, 2021, 39(3): 32. [18] XU R X, LIU T Y, LI L, et al. Document-level event extraction via heterogeneous graph-based interaction model with a tracker[C/OL]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Held Virtually: Association for Computational Linguistics, 2021: 3533-3546. [19] YAO L, MAO C S, LUO Y. Graph convolutional networks for text classification[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Honolulu, USA: AAAI, 2019: 7370-7377. [20] MAAS A L, HANNUN A Y, NG A Y. Rectifier nonlinearities improve neural network acoustic models[C]//Proceedings of the 30th International Conference on Machine Learning. Atlanta, USA: International Machine Learning Society, 2013: 3.
2023, Vol. 63 
