隐式知识图协同过滤模型

doi:10.16451/j.cnki.issn1003-6059.202211008

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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.

Key words： Recommendation System Implicit Knowledge Graph Graph Convolution Collaborative Filtering

Received: 31 July 2022

ZTFLH:

TP 391.3

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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	XUE Feng
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	SANG Sheng

Cite this article:

XUE Feng,SHENG Yicheng,LIU Kang等. Implicit Knowledge Graph Collaborative Filtering Model[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(11): 1033-1041.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202211008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I11/1033

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