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.
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Chen, L., Pu, P. (2010). Eye-Tracking Study of User Behavior in Recommender Interfaces. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_35
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DOI: https://doi.org/10.1007/978-3-642-13470-8_35
Publisher Name: Springer, Berlin, Heidelberg
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