Communications of the ACM
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems
UM '07 Proceedings of the 11th international conference on User Modeling
Critiquing recommenders for public taste products
Proceedings of the third ACM conference on Recommender systems
Recommenders' influence on buyers' decision process
Proceedings of the third ACM conference on Recommender systems
On the role of diversity in conversational recommender systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Consumer decision patterns through eye gaze analysis
Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction
Timing of Adaptive Web Personalization and Its Effects on Online Consumer Behavior
Information Systems Research
RecLab: a system for eCommerce recommender research with real data, context and feedback
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
Content based recommender system by using eye gaze data
Proceedings of the Symposium on Eye Tracking Research and Applications
Identifying objects in images from analyzing the users' gaze movements for provided tags
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Making use of eye tracking information in image collection creation and region annotation
Proceedings of the 20th ACM international conference on Multimedia
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Recommender systems have emerged as an effective decision tool to help users more easily and quickly find products that they prefer, especially in e-commerce environments. However, few studies have tried to understand how this technology has influenced the way users search for products and make purchase decisions. Our current research aims at examining the impact of recommenders by understanding how recommendation tools integrate the classical economic schemes and how they modify product search patterns. We report our work in employing an eye tracking system and collecting users' interaction behaviors as they browsed and selected products to buy from an online product retail website offering over 3,500 items. This in-depth user study has enabled us to collect over 48,000 fixation data points and 7,720 areas of interest from eighteen users, each spending more than one hour on our site. Our study shows that while users still use traditional product search tools to examine alternatives, recommenders definitely provide users with new opportunities in their decision process. More specifically, users actively click and gaze at products recommended to them, up to 40% of the time. In addition, recommendation areas are highly attractive, drawing users to add 50% more items to their baskets as a traditional tool does. Observing that users consult the recommendation area more as they are close to the end of their search process, it seems that recommenders enhance users' decision confidence by satisfying their need for diversity. Based on these results, we derive several interaction design guidelines that can significantly improve users' satisfaction and perception of product recommenders.