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作者:

Meng, Fanyang (Meng, Fanyang.) | Ma, Hongwei (Ma, Hongwei.) | Zhao, Dequn (Zhao, Dequn.) | Deng, Qianhua (Deng, Qianhua.)

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摘要:

To overcome the shortcomings of current approaches in feature extraction, specifically the lack of in-depth feature extraction and the absence of high-order feature aggregation for both users and items, we propose a knowledge graph-based dual-end feature aggregation recommendation model. First, initial vectors are generated using knowledge graph embeddings. Next, a ripple network and a knowledge graph attention network are employed to extract features on the user and item sides, respectively. Then, features of various orders are aggregated and concatenated to obtain the final vectors. Finally, the matching probability between users and items is predicted. Tests performed on three datasets reveal that the new model surpasses seven other baseline models based on AUC, ACC, NDCG@K, and Recall@K metrics. © 2024 IEEE.

关键词:

Graph algorithms Network embeddings Graph embeddings Wiener filtering Collaborative filtering Knowledge graph Graph neural networks

作者机构:

  • [ 1 ] [Meng, Fanyang]Beijing University of Technology, Beijing, China
  • [ 2 ] [Ma, Hongwei]Beijing Institute of Astronautical Systems, Beijing, China
  • [ 3 ] [Zhao, Dequn]Beijing University of Technology, Beijing, China
  • [ 4 ] [Deng, Qianhua]China Telecom, Beijing, China

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年份: 2024

页码: 371-375

语种: 英文

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