=Paper=
{{Paper
|id=Vol-1438/invited
|storemode=property
|title=Recommender Systems Seen Through the Lens of Choice Architecture
|pdfUrl=https://ceur-ws.org/Vol-1438/invited.pdf
|volume=Vol-1438
|dblpUrl=https://dblp.org/rec/conf/recsys/Jameson15
}}
==Recommender Systems Seen Through the Lens of Choice Architecture==
<pdf width="1500px">https://ceur-ws.org/Vol-1438/invited.pdf</pdf>
<pre>
           Recommender Systems Seen Through
              the Lens of Choice Architecture
                                    Anthony Jameson
                             German Research Center for
                              Artificial Intelligence (DFKI)
                                    jameson@dfki.de




                                      Abstract
“How do people make choices?” “How can we help them make better choices?” It’s helpful
to have compact, coherent answers to these questions if we want to build recommender
systems that support choice processes. This talk begins with a brief summary of the
ASPECT and ARCADE models (introduced in “Choice Architecture for Human-Computer
Interaction”), which answer these questions. It then uses this framework to shed new light
on a sample of subtle questions such as: “How can explanations of recommendations help
people make better choices?” and “How can recommender systems help people choose via
trial and error?” The talk is a concrete and selective presentation of key ideas from the
chapter “Human Decision Making and Recommender Systems” in the second edition of the
“Recommender Systems Handbook”.

</pre>