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A novel embedding learning framework for relation completion and recommendation based on graph neural network and multi-task learning
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DOI：10.1007/s00500-021-06617-0.png)
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At present, the relation completion mainly study the influence of single-path or first-order information, but ignoring the more complex relation information widely existing between entities. Meanwhile, recommendation systems based on collaborative filtering algorithms are susceptible to data sparsity issues, leading to a cold start of the system. Therefore, a novel embedding learning framework for relation completion and recommendation based on graph neural network and multi-task learning is proposed. The graph neural network relation completion model GNNRC predicts the relation between entities by embedding learning, which fuses the high-order semantic features of the two target entities' subgraph based on graph neural network. A comparison with TransD model on embeding learning for relation completion is reported. On this basic, the multi-task learning relation recommendation model MLRR add the bridging unit into deep end-to-end recommendation model, which realizes the alternating learning of knowledge graph embedding and recommendation algorithm. Experimental results show that the performance of the proposed recommendation model is significantly better than other baselines. Moreover, strong recommendation performance can be maintained in cold start scenarios where data are sparse.
Keyword:
Knowledge graph
Relation completion
Relation recommendation
Graph neural network
Multi-task learning
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
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