[1]MA Tiantian,YANG Changchun,YAN Xinjie,et al.Research on the fusion of knowledge graph and lightweight graph convolutional network recommendation system[J].CAAI Transactions on Intelligent Systems,2022,17(4):721-727.[doi:10.11992/tis.202107016]
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Research on the fusion of knowledge graph and lightweight graph convolutional network recommendation system
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 4
Page number:
721-727
Column:
学术论文—自然语言处理与理解
Public date:
2022-07-05
- Title:
- Research on the fusion of knowledge graph and lightweight graph convolutional network recommendation system
- Keywords:
- graph convolutional network; knowledge graph; recommendation system; embedded propagation; collaborative filtering; sparsity; neighborhood information; lightweight aggregator
- CLC:
- TP391
- DOI:
- 10.11992/tis.202107016
- Abstract:
- The algorithm based on collaborative filtering is the most important method in the recommendation system. However, the cold start and data sparsity characteristics limit its recommendation performance. We propose a model that combines a knowledge graph and a lightweight graph convolutional network recommendation system to address the aforementioned issues. The model embeds and propagates multiple items in the knowledge graph to obtain more high-order neighborhood information. It aggregates through a lightweight aggregator to predict the score between users and items. Finally, the experimental findings of MovieLens-20M, Last.FM and Book-Crossing on three real datasets show that compared with other benchmark models, this model can achieve better performance.
- References:
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1900-01-01