Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Direct manipulation vs. interface agents
interactions
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 2006 ACM symposium on Applied computing
Evaluating critiquing-based recommender agents
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Each to his own: how different users call for different interaction methods in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
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.