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Modeling and Estimation of Selection Interests through Gaze Behavior

Multiple criteria decision making (MCDM) is a fundamental part of our daily lives. To support solving the problem with conflicting multiple criteria, several analysis methods for MCDM are hitherto proposed. Here, the choice of evaluation criteria is a key to successful decision-making support, including interactive assistance systems. With appropriate evaluation criteria, decision-support systems can help users find an importance weight on each of the criteria and organize their selection interests. This process of preference structuring is helpful for users who want to select a target from alternatives, particularly when they have uncertain preferences and have not yet detailed their needs enough to search targets using appropriate keywords. Here, the questions are how to prepare evaluation criteria beforehand and how to estimate user preferences by using these evaluation criteria. This thesis introduces aspects that represent “why the users look at items,” which provide possible viewpoints of items and proposes a framework for obtaining aspects from users’ gaze behavior while browsing multi-attribute content. In the proposed framework, we introduce an aspect-oriented probabilistic generative model of gaze behavior that is based on topic models. The proposed gaze behavior model learns aspects in a data-driven fashion as a form of the degree of association with each attribute value. Also, this model estimates users’ selection interests by using learned aspects. By estimating users’ selection interests through constructing state space of selection interests from a large amount of users’ gaze behavior, this model can be applied to proactive item recommendation. The following two characteristics in this situation make the design of the gaze behavior model difficult: 1) users do not always compare items in the content, and users do not always focus on all attributes of items; and 2) users’ gaze behavior is affected by the spatial layout of the content. This thesis proposes two kinds of models to overcome these difficulties. For the first difficulty, this thesis introduces a multiscale detection method of users’ comparison behavior named the Multiscale Exact Test (MSET). The idea of MSET is that users’ significant gaze behavior can be detected using the deviations in the distributions of gaze observations by modeling users’ neutral gaze behavior. Users’ comparison behavior can be detected together with its scale by comparing the p-value calculated from observed gaze behavior to the significance level. By regarding the attribute-of-focus detected by the MSET as the observations of generative gaze behavior model, the proposed generative model learns aspects taking into account users’ comparison behavior. We evaluated the validity of this two-step framework of aspect learning using the correlations between learned aspects and task-related attribute values. For the second difficulty, this thesis specifically focuses on the spatial effect of users’ gaze behavior called center bias. The basic idea of this approach is to unify both external factors (i.e., center bias) and internal factors (i.e., preference) in a single generative model. The validity of this proposed framework is evaluated by the prediction accuracy of the region of interests or item of interests through selection interests. Once aspects are learned, the learned aspects can be regarded as evaluation criteria of alternatives of MCDM and can be used for an interactive system that assists users’ MCDM such that the system assists users to recognize the problem structure by directly mentioning aspects. This thesis finally introduces aspectbased interactive assistance using the hierarchical structure of decision problem. By estimating user preferences from gaze behavior and mentioning aspects of alternatives, the system helps users organize their selection interests and decide an alternative to choose. Through interaction during decision making, we show the validity of the effectiveness of using aspects.