=Paper=
{{Paper
|id=Vol-2697/invited1
|storemode=property
|title=Ratings in, rankings out. Keep it simple, they said. But we need more than that (Keynote)
|pdfUrl=https://ceur-ws.org/Vol-2697/invited1.pdf
|volume=Vol-2697
|authors=Christine Bauer
|dblpUrl=https://dblp.org/rec/conf/recsys/Bauer20
}}
==Ratings in, rankings out. Keep it simple, they said. But we need more than that (Keynote)==
https://ceur-ws.org/Vol-2697/invited1.pdf
Ratings in, rankings out. Keep it simple, they said. But we need
more than that.
Christine Bauer
Utrecht University
Utrecht, The Netherlands
ABSTRACT field of Licensing New Media at Austria’s biggest collecting society
Among the many viable research questions in the field of recom- AKM. Christine is an experienced teacher and has been teaching
mender systems, a frequently addressed problem is to accurately a wide spectrum of topics in computing and information systems
predict the relevance of individual items to users, with the goal of across 10 institutions. She engages in mentoring for initiatives such
presenting the assumedly most relevant ones as recommendations. as Women in Music Information Retrieval. More information can
Typically, we have users’ (explicit or implicit) ratings as input and be found at https://christinebauer.eu.
rankings of items as output. Complex enough, yet too simplistic
to reflect reality and indeed meet the various demands in practice.
We have learned that “context matters”. But what does it mean?
What is the context that matters? And how do we get the relevant
signals? It is more than what we currently ascribe to and reflect in
what we call “context-aware recommender systems”. Let’s have a
view to related fields that deal with context as deeply complex input.
And on the output side, we have individual items and also item
bundles, complementaries, sequences, repeated recommendations,
etc. What do we actually want to present? And how? For who?
And why? A ranked list as output may seem like an appropriate
one-size-fits-all solution, does it? In this talk, I will reflect on the
complexity of our research field, reach out to related fields such as
context-aware computing and pervasive advertising for inspiration,
and I will raise a lot of questions that have yet to be answered.
CCS CONCEPTS
• Information systems → Recommender systems.
KEYWORDS
Complex input, Complex output
SPEAKER
Christine Bauer is an assistant professor at Utrecht University, The
Netherlands. Her research activities center on interactive intelligent
systems. In doing so, she takes a human-centered perspective, where
technology follows humans’ and the society’s needs. She focuses
on context-adaptive systems and, currently, on music recommender
systems in particular. Her research and teaching activities are driven
by her interdisciplinary background. She holds a Doctoral degree
in Social and Economic Sciences, a Diploma degree in International
Business Administration, and a Master degree in Business Informat-
ics. Furthermore, she pursued studies in jazz saxophone. Christine
has authored more than 90 scientific papers in refereed journals
and conference proceedings and holds 4 best paper awards as well
as 3 awards for her reviewing activities. Before joining Utrecht
University, she brought her prestigious Elise Richter grant to Jo-
hannes Kepler University Linz, Austria. Earlier she researched at
WU Vienna, Austria, University of Cologne, Germany, and the E-
Commerce Competence Center, Austria. In 2013 and 2015, she was
Visiting Fellow at Carnegie Mellon University, Pittsburgh, PA, USA.
Before starting her academic career, she has built up and led the
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License Attribution 4.0 International (CC BY 4.0).