This tutorial was presented at the RecSys ’22 conference, with Giacomo Balloccu, Gianni Fenu, and Mirko Marras. On the tutorial’s website, you can find the slides, the video recording of our talk, and the notebooks of the hands-on parts.

The goal of this tutorial was to present the RecSys community with recent advances on explainable recommender systems with knowledge graphs. We first introduced conceptual foundations, by surveying the state of the art and describing real-world examples of how knowledge graphs are being integrated into the recommendation pipeline, also for the purpose of providing explanations.

This tutorial continued with a systematic presentation of algorithmic solutions to model, integrate, train, and assess a recommender system with knowledge graphs, with particular attention to the explainability perspective. A practical part provided attendees with concrete implementations of recommender systems with knowledge graphs, leveraging open-source tools and public datasets; in this part, tutorial participants were engaged in the design of explanations accompanying the recommendations and in articulating their impact.

We concluded the tutorial by analyzing emerging open issues and future directions.