RecSys '19 joint workshop on interfaces and human decision making for recommender systems

Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Martijn C. Willemsen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademic

Samenvatting

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users' perspectives. The field has reached a point where it is ready to look beyond algorithms, into users' interactions, decision making processes, and overall experience. This workshop will focus on the "human side" of recommender systems research. The workshop goal is to improve users' overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems.
Originele taal-2Engels
TitelRecSys 2019 - 13th ACM Conference on Recommender Systems
Plaats van productieNew York
UitgeverijAssociation for Computing Machinery, Inc
Pagina's560-561
Aantal pagina's2
ISBN van elektronische versie9781450362436
ISBN van geprinte versie978-1-4503-6243-6
DOI's
StatusGepubliceerd - 10 sep 2019
EvenementRecSys '19 ACM Conference on Recommender Systems - Copenhagen, Denemarken
Duur: 16 sep 201920 sep 2019

Congres

CongresRecSys '19 ACM Conference on Recommender Systems
LandDenemarken
StadCopenhagen
Periode16/09/1920/09/19

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  • Citeer dit

    Brusilovsky, P., de Gemmis, M., Felfernig, A., Lops, P., O'Donovan, J., Semeraro, G., & Willemsen, M. C. (2019). RecSys '19 joint workshop on interfaces and human decision making for recommender systems. In RecSys 2019 - 13th ACM Conference on Recommender Systems (blz. 560-561). Association for Computing Machinery, Inc. https://doi.org/10.1145/3298689.3346971