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• W2893775232 abstract "Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results." @default.
• W2893775232 created "2018-10-05" @default.
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• W2893775232 date "2018-09-27" @default.
• W2893775232 modified "2023-10-15" @default.
• W2893775232 title "Recurrent knowledge graph embedding for effective recommendation" @default.
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• W2893775232 doi "https://doi.org/10.1145/3240323.3240361" @default.
• W2893775232 hasPublicationYear "2018" @default.
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