Abstract
While eye gaze data contain promising clues for inferring the interests of viewers of digital catalog content, viewers often dynamically switch their focus of attention. As a result, a direct application of conventional behavior analysis techniques, such as topic models, tends to be affected by items or attributes of little or no interest to the viewer. To overcome this limitation, we need to identify “when” the user compares items and to detect “which attribute types/values” reflect the user’s interest. This paper proposes a novel two-step approach to addressing these needs. Specifically, we introduce a likelihood-based short-term analysis method as the first step of the approach to simultaneously determine comparison phases of browsing and detect the attributes on which the viewer focuses, even when the attributes cannot be directly obtained from gaze points. Using probabilistic latent semantic analysis, we show that this short-term analysis step greatly improves the results of the subsequent step. The effectiveness of the framework is demonstrated in terms of the capability to extract combinations of attributes relevant to the viewer’s interest, which we call aspects, and also to estimate the interest described by these aspects.
Highlights
Estimating the real-time interest of users browsing a digital catalog opens a variety of application possibilities including online recommendation of items that might better fit their needs and automated assistance, such as offering a new viewpoint for their choice (Misu et al, 2011; Reusens, Lemahieu, Baesens, & Sels, 2017; Walker et al, 2004)
This paper addresses two problems related to using eye gaze data collected during digital catalog browsing: (1) data-driven extraction of aspects that describe user interest and (2) estimation of the user interest
The results presented above demonstrate the effectiveness of introducing the AOF detection step for learning aspects from gaze behavioral data and its effectiveness in estimating user interests reflected in gaze-target patterns
Summary
Estimating the real-time interest of users browsing a digital catalog opens a variety of application possibilities including online recommendation of items that might better fit their needs and automated assistance, such as offering a new viewpoint for their choice (Misu et al, 2011; Reusens, Lemahieu, Baesens, & Sels, 2017; Walker et al, 2004). Bring such systems into reality requires the development of a representation of user interest and a method for estimating it.
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