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ABSTRACT
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.

Switzerland 2010 HCI Usability Tobii eye tracking 1750 recommendation Recommender product search evaluate understanding attention usage user

in list: HCI & Usability

ABSTRACT
Recommender systems, as a type of Web personalized service to support users’ online product searching, have been widely developed in recent years but with primary emphasis on algorithm accuracy. In this paper, we particularly investigate the efficacy of recommender interface designs in affecting users’ decision making strategies through the observation of their eye movements and product selection behavior. One interface design is the standard list interface where all recommended items are listed one by one. Another two are layout variations of organization-based interface where recommendations are grouped into categories. The eye-tracking user evaluation shows that the organization interfaces, especially the one with a quadrant layout, can significantly attract users’ attentions to more items, with the resulting benefit to enhance their objective decision quality.

Switzerland 2010 Tobii eye tracking 1750 product searching Recommender layout interface organization design HCI Usability

in list: HCI & Usability

Abstract
Acquiring relevant information to keep user’s preferences up-to-date is crucial in recommender systems in order to close the cycle of recommendations. Ambient Intelligence is a suitable approach for non-intrusively closing the loop in recommender systems using ambient eye-trackers. We combine a method for acquiring relevance feedback through eye-tracking with the functionalities of an extractor agent. We describe the results of experiments conducted in a recommender system to obtain implicit feedback using eye fixations. Finally, we obtain a ranking of user’s most relevant preferences and behaviours.

HCI User Modelling Ambient Recommender Systems Implicit Relevance Feedback Eye Tracking spain 2005 Tobii 1750

in list: HCI & Usability