Explainable Entity-based Recommendations with Knowledge Graphs

Abstract

Explainable recommendation is an important task. Many methods have been proposed which generate explanations from the content and reviews written for items. When review text is unavailable, generating explanations is still a hard problem. In this paper, we illustrate how explanations can be generated in such a scenario by leveraging external knowledge in the form of knowledge graphs. Our method jointly ranks items and knowledge graph entities using a Personalized PageRank procedure to produce recommendations together with their explanations.

Accepted for publication in the 11th ACM Conference on Recommender Systems (RecSys 2017) - Posters

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