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  4. How Users Perceive and Appraise Personalised Recommendations
 
conference poster not in proceedings

How Users Perceive and Appraise Personalised Recommendations

Nicolas, Jones  
•
Pearl, Pu  
•
Li, Chen
2009
User Modelling, Adaptation and Personalization

Traditional websites have long relied on users revealing their preferences explicitly through direct manipulation interfaces. However recent recommender systems have gone as far as using implicit feedback indicators to understand users' interests. More than a decade after the emergence of recommender systems, the question whether users prefer them compared to stating their preferences explicitly, largely remains a subject of study. Even though some studies were found on users' acceptance and perceptions of this technology, these were general marketing-oriented surveys. In this paper we report an in-depth user study comparing Amazon's implicit book recommender with a baseline model of explicit search and browse. We address not only the question "do people accept recommender systems" but also how or under what circumstances they do and more importantly, what can still be improved.

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Type
conference poster not in proceedings
Author(s)
Nicolas, Jones  
Pearl, Pu  
Li, Chen
Date Issued

2009

Subjects

Recommender Systems

•

preference elicitation

•

interaction design

•

implicit

•

explicit

•

user study

•

user modeling

•

Amazon

•

automation

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direct manipulation

•

past purchases

Written at

EPFL

EPFL units
GR-PU  
Event nameEvent placeEvent date
User Modelling, Adaptation and Personalization

Trento, Italy

June 22-26, 2009

Available on Infoscience
June 25, 2009
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/40917
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