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.
Fund:National Natural Science Foundation of China(No.62272143), University Synergy Innovation Program of Anhui Province(No.GXXT-2020-014), Major Science and Technology Project of Anhui Province(No.202203a05020025), the Seventh Special Support Plan for Innovation and Entrepreneurship in Anhui Province
Corresponding Authors:
XUE Feng,Ph.D., professor. His research interests include artificial intelligence, multimedia analysis and recommendation system.)
About author:: SHENG Yicheng, master student. His research interests include recommendation system and data mining. LIU Kang, Ph.D. candidate. His research interests include recommendation system, data mining and multimedia analysis.SANG Sheng, Ph.D. candidate. His research interests include recommendation system, data mining and multimedia analysis.
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2022, Vol. 35 
