Hands on Explainable Recommender Systems with Knowledge Graphs

Balloccu G.;Boratto L.;Fenu G.;Marras M.
2022-01-01

Abstract

The goal of this tutorial is to present the RecSys community with recent advances on 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. Website: https://explainablerecsys.github.io/recsys2022/.
2022
Inglese
RecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems
9781450392785
Association for Computing Machinery, Inc
New York
Hands on Explainable Recommender Systems with Knowledge Graphs
Giacomo Balloccu; Ludovico Boratto, Gianni Fenu, Mirko Mirko
710
713
4
The 16th ACM Conference on Recommender Systems
Comitato scientifico
September
Seattle
internazionale
scientifica
Explainability
Knowledge Graphs
Recommender Systems
Responsible Recommendation.
no
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Balloccu, G.; Boratto, L.; Fenu, G.; Marras, M.
273
4
4.1 Contributo in Atti di convegno
none
info:eu-repo/semantics/conferencePaper
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