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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
A better understanding of the human user’s expectations and sensitivities to the real-time behavior generated by virtual agents can provide insightful empirical data and infer useful principles to guide the design of intelligent virtual agents. In light of this, we propose and implement a research framework to systematically study and evaluate different important aspects of multimodal real-time interactions between humans and virtual agents. Our platform allows the virtual agent to keep track of the user’s gaze and hand movements in real time, and adjust his own behaviors accordingly. Multimodal data streams are collected in human-avatar interactions including speech, eye gaze, hand and head movements from both the human user and the virtual agent, which are then used to discover fine-grained behavioral patterns in human-agent interactions. We present a pilot study based on the proposed framework as an example of the kinds of research questions that can be rigorously addressed and answered. This first study investigating human-agent joint attention reveals promising results about the role and functioning of joint attention in human-avatar interactions.

USA 2010 Tobii eye tracking 1750 HCI evaluate multimodal real-time interactions virtual avatar attention

in list: HCI & Usability