Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-10 Cooperative journals: 《计算机应用研究》
Abstract： Applying knowledge graph to recommendation system can make use of semantic relations between entities of knowledge graph to learn user and item representation. The embedding propagation method uses the graph structure of the knowledge graph to learn relevant features, but the semantic dependency between multi-hop entities decreases as the propagation range increases. In order to effectively improve the semantic expression ability of recommendation and improve the accuracy of recommendation, this paper proposes a knowledge-aware propagation recommendation algorithm based on users' potential interests. The model adopts heterogeneous propagation method to disseminate item relevant knowledge and iteratively learn users' potential interests, so as to enhance the representation ability of the model to users and items. Specifically, first, graph embedding layer generate initialize representation of users and items, and in the heterogeneous propagation layer, the knowledge-aware attention mechanism can distinguish the importance of entities in the same layer, so the model can generate the representation of target entities more accurately. Then the user's potential interest propagation can effectively learn the user's higher-order potential interest and enhance the semantic relevance of multi-hop entities. Finally, information decay factor is used in the prediction layer to distinguish the importance of different communication levels and generate the final representation of users and items. Experiments show that the AUC value of the model on the Last. FM and Book-Crossing increases by 2.25% and 4.71% compared with the most advanced baseline, and the F1 value increases by 3.05% and 1.20% respectively, and the Recall@K value is superior to the comparison baseline model. The proposed model can effectively improve the accuracy of recommendation.
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