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• W3120355614 abstract "This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture user historical item features and path-based context to characterise next purchased item. Extensive evaluations of TMER on three real-world benchmark datasets show state-of-the-art performance compared against recent strong baselines." @default.
• W3120355614 created "2021-01-18" @default.
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• W3120355614 date "2021-01-05" @default.
• W3120355614 modified "2023-09-23" @default.
• W3120355614 title "Temporal Meta-path Guided Explainable Recommendation" @default.
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• W3120355614 doi "https://doi.org/10.48550/arxiv.2101.01433" @default.
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