[1]SHU Wei,LI Xiang,SUN Jizhou,et al.Recommendation method based on dynamic interest propagation and knowledge graph[J].CAAI Transactions on Intelligent Systems,2024,19(4):997-1006.[doi:10.11992/tis.202209061]
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Recommendation method based on dynamic interest propagation and knowledge graph

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 997-1006 Column: 学术论文—知识工程 Public date: 2024-07-05
Title:
Recommendation method based on dynamic interest propagation and knowledge graph
Author(s):
SHU Wei;  LI Xiang;  SUN Jizhou;  ZHU Quanyin;  REN Ke
Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223003, China
Keywords:
dynamic interest propagation; recommendation method; knowledge graph; entity semantic; attention mechanism; user interest; multi-task learning; knowledge graph embedding
CLC:
TP391.3
DOI:
10.11992/tis.202209061
Abstract:
As an information filtering method, knowledge graph recommendation is widely used in the fields of e-commerce and social networking. However, most knowledge graph-based recommendation methods did not adopt appropriate strategies to solve the problem of entity semantic relevance decay during the propagation process. Additionally, single-dimensional modeling could not utilize knowledge graph to enrich user and item representations at the same time. Therefore, we propose RDPKG, a recommendation method based on dynamic interest propagation and knowledge graph. Specifically, RDPKG employs a propagation network to mine user interests of different layers to generate use representation; and applies an attention mechanism to distinguish the importance of user interests under different propagation layers. Then, RDPKG employs a cross-compression unit to extract valid information in the knowledge graph to generate item representation, and applies multi-task learning to optimize the recommendation unit and the knowledge graph embedding unit. Last, RDPKG takes the inner product of the final user representation and the item representation to obtain the interaction probability. Comparative experiments on three real-world public datasets in the field of recommender systems were carried out. The results demonstrate that the accuracy of RDPKG in the click-through rate prediction task has reached 85.42%, 76.09% and 69.39% respectively. RDPKG outperforms other comparison methods, which fully verifies the validity of RDPKG method.
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