Implicit Knowledge Graph Collaborative Filtering Model
XUE Feng1,2, SHENG Yicheng3, LIU Kang3, SANG Sheng3
1. School of Software, Hefei University of Technology, Hefei 230009; 2. Hefei Comprehensive National Science Center, Hefei 230088; 3. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601
Abstract：In the existing recommendation methods based on knowledge graphs, graph neural networks are utilized to capture the correlation between user preferences and knowledge entities to achieve optimal recommendation results. However, certain limitations occur in this kind of relevance modeling methods due to its dependence on the explicit relationship between nodes(users, items or entities). To address these problems, an implicit knowledge graph collaborative filtering model(IKGCF) is proposed. Firstly, the implicit collaborative knowledge graph is constructed to eliminate the interference of explicit relationship on implicit interaction in recommendations and remove the limitation of explicit relationship on semantic relevance in the graph. Then, an enhanced graph neural network module is adopted to perform neighbor aggregation and message propagation to better capture the higher-order relevance on the implicit collaborative knowledge graph. Finally, a layer selection mechanism is employed to obtain the final node embedding vectors and predict and optimize the model. Experiments on three public datasets show that IKGCF achieves better performance. The full code of IKGCF is open-sourced at https://github.com/hfutmars/IKGCF.
[1] HE X N, LIAO L Z, ZHANG H W, et al. Neural Collaborative Filtering // Proc of the 26th International Conference on World Wide Web. New York, USA: ACM, 2017: 173-182. [2] HE X N, HE Z K, SONG J K, et al. NAIS: Neural Attentive Item Similarity Model for Recommendation. IEEE Transactions on Know-ledge and Data Engineering, 2018, 30(12): 2354-2366. [3] WANG X, HE X N, WANG M, et al. Neural Graph Collaborative Filtering // Proc of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2019: 165-174. [4] ZHANG F Z, YUAN N J, LIAN D F, et al. Collaborative Know-ledge Base Embedding for Recommender Systems // Proc of the 22nd ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining. New York, USA: ACM, 2016: 353-362. [5] AI Q Y, AZIZI V, CHEN X, et al. Learning Heterogeneous Know-ledge Base Embeddings for Explainable Recommendation. Algorithms, 2018, 11(9). DOI: 10.3390/a11090137. [6] CAO Y X, WANG X, HE X N, et al. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences // Proc of the World Wide Web Conference. New York, USA: ACM, 2019: 151-161. [7] HUANG J, ZHAO W X, DOU H J, et al. Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks // Proc of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2018: 505-514. [8] WANG X, WANG D X, XU C R, et al. Explainable Reasoning over Knowledge Graphs for Recommendation // Proc of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI, 2019: 5329-5336. [9] HU B B, SHI C, ZHAO W X, et al. Leveraging Meta-Path Based Context for Top-N Recommendation with a Neural Co-attention Mo-del // Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2018: 1531-1540. [10] SUN Z, YANG J, ZHANG J, et al. Recurrent Knowledge Graph Embedding for Effective Recommendation // Proc of the 12th ACM Conference on Recommender Systems. New York, USA: ACM, 2018: 297-305. [11] WANG H W, ZHANG F Z, WANG J L, et al. RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems // Proc of the 27th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2018: 417-426. [12] YU X, REN X, SUN Y Z, et al. Personalized Entity Recommendation: A Heterogeneous Information Network Approach // Proc of the 7th ACM International Conference on Web Search and Data Mining. New York, USA: ACM, 2014: 283-292. [13] ZHAO H, YAO Q M, LI J D, et al. Meta-Graph Based Reco-mmendation Fusion over Heterogeneous Information Networks // Proc of the 23rd ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining. New York, USA: ACM, 2017: 635-644. [14] JIA Y T, WANG Y Z, JIN X L, et al. Knowledge Graph Embe-dding: A Locally and Temporally Adaptive Translation-Based Approach. ACM Transactions on the Web, 2018, 12(2). DOI: 10.1145/3132733. [15] KIPF T N, WELLING M.Semi-Supervised Classification with Graph Convolutional Networks[C/OL]. [2022-06-20].https://arxiv.org/pdf/1609.02907.pdf. [16] VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph Attention Networks[C/OL].[2022-06-20]. https://arxiv.org/pdf/1710.10903.pdf. [17] WANG X, HE X N, CAO Y, et al. KGAT: Knowledge Graph Attention Network for Recommendation // Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2019: 950-958. [18] HE X N, DENG K, WANG X, et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation // Proc of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2020: 639-648. [19] LIU K, XUE F, GUO D, et al. MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation. ACM Transactions on Information Systems, 2022. DOI: 10.1145/3544106. [20] LIU K, XUE F, HE X N, et al. Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation. IEEE Transactions on Computational Social Systems, 2022. DOI: 10.1109/TCSS.2022.3151822. [21] RENDLE S, GANTNER Z, FREUDENTHALER C, et al. Fast Context-Aware Recommendations with Factorization Machines // Proc of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2011: 635-644. [22] HE X N, CHUA T S.Neural Factorization Machines for Sparse Predictive Analytics // Proc of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2017: 355-364.
2022, Vol. 35 
