Learning aspects of interest from Gaze

Shimonishi, Kei; Kawashima, Hiroyuki; Yonetani, Ryo; Ishikawa, Erina; Matsuyama, Takashi

doi:10.1145/2535948.2535955

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Regarding observation model

, one can directly apply a categorical distribution similar to the original pLSA [ 42 ], and in fact, our previous work followed this option [ 11 ]. However, such a model seems to be unnatural as a user’s focus can take only a single attribute value at a time.…”

Section: Methodsmentioning

confidence: 99%

“…The generative model used in the second step is an extension of our previous work (Shimonishi, Kawashima, Yonetani, Ishikawa, & Matsuyama, 2013), which took all attribute values of items into account. In contrast, the basic idea now is to use the AOF behind the gaze behavior as the "observation" of the subsequent probabilistic generative model.…”

Section: Contributionsmentioning

confidence: 99%

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A two-step approach for interest estimation from gaze behavior in digital catalog browsing

Shimonishi¹,

Kawashima²

2020

JEMR

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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.

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“…Regarding observation model

Section: Methodsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

A two-step approach for interest estimation from gaze behavior in digital catalog browsing

Shimonishi¹,

Kawashima²

2020

JEMR

Self Cite

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show abstract

“…To achieve the information system described above, we need to investigate how to model viewers states during content browsing. For modeling viewers' states, we consider to introduce findings from existing research on user state modeling to our framework [17].…”

Section: Potential Applications Of the Proposed Frameworkmentioning

confidence: 99%

Using Designed Structure of Visual Content to Understand Content-Browsing Behavior

Ishikawa

Kawashima

Matsuyama

2015

IEICE Trans. Inf. & Syst.

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SUMMARYStudies on gaze analysis have revealed some of the relationships between viewers' gaze and their internal states (e.g., interests and intentions). However, understanding content browsing behavior in uncontrolled environments is still challenging because human gaze can be very complex; it is affected not only by viewers' states but also by the spatiosemantic structures of visual content. This study proposes a novel gaze analysis framework which introduces the content creators' point of view to understand the meaning of browsing behavior. Visual content such as web pages, digital articles and catalogs are comprised of structures intentionally designed by content creators, which we refer to as designed structure. This paper focuses on two design factors of designed structure: spatial structure of content elements (content layout), and their relationships such as "being in the same group". The framework was evaluated with an experiment involving 12 participants, wherein the participant's state was estimated from their gaze behavior. The results from the experiment show that the use of design structure improved estimation accuracies of user states compared to other baseline methods.

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Gaze Scanpath Transformer: Predicting Visual Search Target by Spatiotemporal Semantic Modeling of Gaze Scanpath

Nishiyasu,

Sato

2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Learning aspects of interest from Gaze

Cited by 4 publications

References 5 publications

A two-step approach for interest estimation from gaze behavior in digital catalog browsing

A two-step approach for interest estimation from gaze behavior in digital catalog browsing

Using Designed Structure of Visual Content to Understand Content-Browsing Behavior

Gaze Scanpath Transformer: Predicting Visual Search Target by Spatiotemporal Semantic Modeling of Gaze Scanpath

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