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Vol-2697
urn:nbn:de:0074-2697-3
Copyright © 2020 for
the individual papers by the papers' authors.
Copyright © 2020 for the volume
as a collection by its editors.
This volume and its papers are published under the
Creative Commons License Attribution 4.0 International
(CC BY 4.0).
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ComplexRec-ImpactRS 2020
Recommendation in Complex Scenarios and the Impact of Recommender Systems 2020
Proceedings of the Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems
co-located with 14th ACM Conference on Recommender Systems (RecSys 2020)
Online, September 25, 2020.
Edited by
Toine Bogers *
Marijn Koolen **
Casper Petersen ***
Bamshad Mobasher ****
Alexander Tuzhilin *****
Oren Sar Shalom ******
Dietmar Jannach *******
Joseph A. Konstan ********
* Aalborg University Copenhagen, Department of Communication and Psychology, Copenhagen, Denmark
** Royal Netherlands Academy of Arts and Sciences - Humanities Cluster, Amsterdam, Netherlands
*** Sampension, Copenhagen, Denmark
**** DePaul University, School of Computing, Chicago, IL, USA
***** New York University, Stern School of Business, New York City, NY, USA
****** Facebook, USA
******* University of Klagenfurt, Department of Applied Informatics, Austria
******** University of Minnesota, Department of Computer Science and Engineering, MN, USA
Table of Contents
Session 1: ComplexRec 2020
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Ratings in, rankings out. Keep it simple, they said. But we need more than that (Keynote)
Christine Bauer
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Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning
Oleksii Moskalenko,
Diego Saez-Trumper,
Denis Parra
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CuratorNet: Visually-aware Recommendation of Art Images
Denis Parra,
Pablo Messina,
Manuel Cartagena,
Felipe del Rio,
Patricio Cerda-Mardini
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Beyond algorithms: Ranking at scale at Booking.com
Themis Mavridis,
Soraya Hausl,
Andrew Mende,
Roberto Pagano
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Personalizing Item Recommendation via Price Understanding
Soumya Wadhwa,
Ashish Ranjan,
Selene Xu,
Jason H.D. Cho,
Sushant Kumar,
Kannan Achan
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Making Sense of the Urban Future: Recommendation Systems in Smart Cities
Dirk Ahlers
Session 2: ImpactRS 2020
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Exploring Artist Gender Bias in Music Recommendation
Dougal Shakespeare,
Lorenzo Porcaro,
Emila Gomez,
Carlos Castillo
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div2vec: Diversity-Emphasized Node Embedding
Jisu Jeong,
Jeong-Min Yun,
Hongi Keam,
Young-Jin Park,
Zimin Park,
Junki Cho
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Intelligent Recommendations for Citizen Science
Naama Dayan,
Kobi Gal,
Avi Segal,
Guy Shani,
Darlene Cavalier
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Maximizing the Engagement: Exploring New Signals of Implicit Feedback in Music Recommendations
Andres Ferraro,
Sergio Oramas,
Massimo Quadrana,
Xavier Serra
2020-09-27: submitted by Casper Petersen,
metadata incl. bibliographic data published under Creative Commons CC0
2020-10-12: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)
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