HAN Chen, YANG Xingyao, YU Jiong, GUO Liang, HU Haoyu. Knowledge graph double perception network for recommendation algorithm[J]. Microelectronics & Computer, 2022, 39(8): 11-20. DOI: 10.19304/J.ISSN1000-7180.2022.0096
Citation: HAN Chen, YANG Xingyao, YU Jiong, GUO Liang, HU Haoyu. Knowledge graph double perception network for recommendation algorithm[J]. Microelectronics & Computer, 2022, 39(8): 11-20. DOI: 10.19304/J.ISSN1000-7180.2022.0096

Knowledge graph double perception network for recommendation algorithm

  • In recent years, excellent results have been achieved by aggregating the additional item information in the knowledge graph, but there are relatively few sources of user information. At the same time, multiple aggregation will make the expression of the characteristics of the item incomplete and even produce noise. Aiming at the above two points, a KGDP recommendation algorithm based on knowledge graph is proposed. Firstly, some items are randomly selected from user interaction records as user related items, and the neighbor entities of the items are selected as item related entities; Then, the selected user related items are fused into user features through deep neural network, which enriches user features and aggregates the related entities of the items separately; Secondly, through two deep neural networks, users can perceive item characteristics and neighbor characteristics respectively, that is, non-linear interaction. Finally, a single-layer perceptron is used to adjust the output weight of interactive features for score prediction. Experiments on two real datasets commonly used in recommendation algorithm, compared with the baseline model, the AUC index improved by 9.2% and 2.4% respectively; ACC index improved by 6.6% and 1.9%; F1 index improved by 7.0% and 1.1% respectively; Precision@N index improved by 28.8% and 6.5% respectively; Recall@N index improved by 4.0% and 23.7% respectively; F1@N index improved by 43.3% and 8.4% respectively.
  • loading

Catalog

    Turn off MathJax
    Article Contents

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return