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Résumé : Since it can effectively address the problem of sparsity and cold start of collaborative filtering, knowledge graph (KG) is widely studied and employed as side information in the field of recommender systems. However, most of existing KG-based recommendation methods mainly focus on how to effectively encode the knowledge associations in KG, without highlighting the crucial collaborative signals which are latent in user-item interactions. As such, the learned embeddings underutilize the two kinds of pivotal information and are insufficient to effectively represent the latent semantics of users and items in vector space.