=Paper=
{{Paper
|id=Vol-2960/keynote_kars_keynote
|storemode=property
|title=How Can I Help You? Knowledge Graphs for Explainable Recommendation (Abstract)
|pdfUrl=https://ceur-ws.org/Vol-2960/keynote_kars_keynote.pdf
|volume=Vol-2960
|authors=Gerard de Melo
|dblpUrl=https://dblp.org/rec/conf/recsys/Melo21
}}
==How Can I Help You? Knowledge Graphs for Explainable Recommendation (Abstract)==
<pdf width="1500px">https://ceur-ws.org/Vol-2960/keynote_kars_keynote.pdf</pdf>
<pre>
How Can I Help You? Knowledge Graphs for
Explainable Recommendation
Gerard de Melo1
1
    University of Potsdam, Germany


                                         Abstract
                                         In our complex modern world, we increasingly rely on automated systems to guide us in our decision-
                                         making. Intelligent recommender systems require knowledge about the users and about potential items
                                         of interest, but can also benefit from various sorts of background knowledge, e.g., how different human
                                         activities relate to one another. In this talk, I discuss several methods to bring together these various
                                         kinds of knowledge by means of knowledge graphs and neuro-symbolic explainable AI. Such methods
                                         draw on deep reinforcement learning or neural logic reasoning to provide explanations that allow users
                                         to better understand why particular items are being recommended. I will also discuss our work on how
                                         to mitigate bias and enable dialogue-based interaction in conversational recommender systems, along
                                         with datasets and code that we have released to promote further research on these topics.




1. Speaker
Gerard de Melo is a professor at the Hasso Plattner Institute for Digital Engineering and the
University of Potsdam, Germany, where he holds the Chair for Artificial Intelligence and
Intelligent Systems. He has published over 150 papers, with Best Paper awards at CIKM 2010,
ICGL 2008, the EACL 2021 LANTERN Workshop, the NAACL 2015 Workshop on Vector Space
Modeling, as well as the WWW 2011 Best Demonstration Award, among others. Previously, he
was a professor at Rutgers University in New Jersey and at Tsinghua University in Beijing, and
a Post-Doctoral Research Scholar at ICSI/UC Berkeley. He received his doctoral degree at the
Max Planck Institute for Informatics. Further details are available at http://gerard.demelo.org/.




KaRS & ComplexRec ’21: Workshops on Knowledge-aware and Conversational Recommender Systems (KaRS ’21) and
Recommendation in Complex Scenarios (ComplexRec ’21) co-located with the 15th ACM Conference on Recommender
Systems (RecSys 2021)
                                       © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
    CEUR
    Workshop
    Proceedings
                  http://ceur-ws.org
                  ISSN 1613-0073       CEUR Workshop Proceedings (CEUR-WS.org)

</pre>