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    Knowledge Graph Representation Reasoning for Recommendation System

    Tao Li, Hao Li*, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu

    Journal of New Media, Vol.2, No.1, pp. 21-30, 2020, DOI:10.32604/jnm.2020.09767

    Abstract In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms, we propose an efficient KGRS model. KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm, then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning, then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning. Finally, through the full connection layer and sigmoid function to get the prediction ratings, More >

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