Abstract:
Recommender systems model user preferences to reduce information explosion and improve online application user experience. Recommender systems still face data sparsity and cold-start issues despite many efforts. Recommending with knowledge graph side information has gained popularity in recent years. This method can solve the above issues and explain recommended items. Knowledge graph-based recommender systems are systematically surveyed in this paper. We classify recent papers into embedding-, connection-, and propagation-based methods. We also subdivide each category by approach characteristics. We also examine how the proposed algorithms use the knowledge graph to make accurate and explainable recommendations. We conclude with several research avenues.
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