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An efficient joint framework for interacting knowledge graph and item recommendation

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Abstract

Incorporating knowledge graphs in recommendation systems is promising as knowledge graphs can be a side information for recommendation systems to alleviate the sparsity and the cold start problems. However, existing works essentially assume that side information (i.e.,  knowledge graphs) is completed, which may lead to sub-optimal performance. Meanwhile, semantic hierarchies implied in applications are prevalent, and many existing approaches fail to model this semantic characteristic. Modeling the semantic structure between items in recommendation systems is a crucial challenge. Therefore, it is crucial to solve the incompleteness of knowledge graphs when integrating it into recommendation system as well as to represent the hierarchical structure contained in items. In this paper, we propose Paguridae, a framework that utilizes the item recommendation task to assist link prediction task. A core idea of the Paguridae is that two tasks automatically share the potential features between items and entities. We adopt two main structures to model the hierarchy between items and entities. In order to model the hierarchy in items, we adopt graph convolutional networks as a representation learning method. In order to model the hierarchy in entities, we use Hirec model, which maps entities into the polar coordinate system. Under the framework, users can get better recommendations and knowledge graphs can be completed as these two tasks have a mutual effect. Experiments on two real-world datasets show that the Paguridae can be trained substantially, improving F1-score by 62.51% and precision by 49.31% compared to the state-of-the-art methods.

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  1. School of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Haizhou Du, Yue Tang & Zebang Cheng

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  1. Haizhou Du
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  2. Yue Tang
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Du, H., Tang, Y. & Cheng, Z. An efficient joint framework for interacting knowledge graph and item recommendation. Knowl Inf Syst 65, 1685–1712 (2023). https://doi.org/10.1007/s10115-022-01808-z

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