Explores improving recommendations using knowledge graphs to overcome data issues.
Recently, much attention has been paid to the usage of knowledge graph within the context of recommender systems to alleviate the data sparsity and cold-start problems. However, when incorporating entities from a knowledge graph to represent users, most existing works are unaware of the relationships between these entities and users. As a result, the recommendation results may suffer a lot from some unrelated entities.
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Tang et al. (2019) studied this question.
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