Explainable Entity-based Recommendations with Knowledge Graphs

By Rose Catherine, Kathryn Mazaitis, Maxine Eskénazi, William W. Cohen. 2017

56 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “Catherine et al. [2017] illustrated how explanations can be generated by leveraging external knowledge in the form of knowledge graphs.”
AliCoCo: Alibaba E-commerce Cognitive Concept Net (2020, methodology): “Inspired by the construction of opendomain KGs, different kinds of KGs in e-commerce are constructed to describe relations among users, items and item attributes [1, 6].”
Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation (2019, background): “[3] leverage knowledge graphs to generate recommendations together with their explanations with Personalized PageRank.”
Learning over Knowledge-Base Embeddings for Recommendation (2018, methodology): “[8] proposed to jointly rank items and knowledge graph entities using a personalized page-rank procedure to produce recommendations together with the explanations.”
A historical perspective of explainable Artificial Intelligence (2020, background): “Knowledge-based explanations for recommender systems (Catherine, Mazaitis, Eskénazi, & Cohen, 2017) make use of knowledge graphs that relate item properties and users' behavior in terms of their past interactions with items.”
Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “For the ease of presentation, all our examples are from the domain of our evaluation dataset, movies, as this has been a fertile area of recommendation systems research both from an algorithmic and from an explainability perspective [6, 8, 17, 22, 48, 51].”