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In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation (TMER), which 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.
Many of existing methods which exploit meta-path guided strategy have shown promising results.
In this paper, we propose a heterogeneous proximity preserving network embedding model via meta path guided random walk, which is capable of capturing the high-order proximity between vertices specified by the given path.