The goal of this tutorial is to present the RecSys community with recent advances on the development and evaluation of explainable recommender systems with knowledge graphs. We will first introduce 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 will continue 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 will then provide attendees with concrete implementations of recommender systems with knowledge graphs, leveraging open-source tools and public datasets; in this part, tutorial participants will be engaged in the design of explanations accompanying the recommendations and in articulating their impact. We conclude the tutorial by analyzing emerging open issues and future directions.
This beginner/intermediate-level tutorial is accessible to researchers, technologists and practitioners. For people not familiar with recommender systems, this tutorial covers necessary background material. Moreover, no prior knowledge on explanations, knowledge graphs, and recommender systems with knowledge graphs is assumed. Basic knowledge of Python programming and of quite common libraries, such as Pandas and NumPy, is preferred. One aspect relevant from the outline is that the explainability perspective of our tutorial is an interdisciplinary topic, touching on several dimensions beyond algorithms and being of interest for people with different backgrounds.
This tutorial will take place on September 18, 2022 in Seattle, WA, USA, as part of the 16th ACM Conference on Recommender Systems (RecSys 2022).
|5 mins||Welcome and Presenters' Introduction|
|60 mins||Session I: Foundations|
Introduction to explainable recommendation (20 mins)
Explainable recommendation models with knowledge graphs (20 mins)
Explainable recommendation evaluation (20 mins)
Questions and Discussion
|30 mins||Coffee Break|
|65 mins||Session 2: Hands-on Case Studies|
Recommendation models in practice (35 mins)
Creation and impact of explanations (30 mins)
|10 mins||Challenges, Final Remarks, and Discussion|
The material accompanying this tutorial will be published here right after the live sessions.
Registration to the tutorial will be managed by the RecSys 2022 main conference organization. Registration is yet to open.
Please, reaching out to us at email@example.com, firstname.lastname@example.org, email@example.com, and firstname.lastname@example.org for any request you might have.