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Proceedings of the ACM Web Conference 2022
To facilitate human decisions with credible suggestions, personalized recommender systems should have the ability to generate corresponding explanations while making recommendations. Knowledge graphs (KG), which contain comprehensive information about users and products, are widely used to enable this. By reasoning over a KG in a node-by-node manner, existing explainable models provide a KG-grounded path for each user-recommended item. Such paths serve as an explanation and reflect thedoi:10.1145/3485447.3511937 fatcat:3tqe4t4uqzedljhq3xhs2lfnj4