A Bayesian approach for user modeling in dialogue systems

Akiba, Tomoyosi; Hozumi, Takeshi

doi:10.3115/991250.991348

Cited by 23 publications

(11 citation statements)

References 6 publications

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“…For the Lumière Project [7] a BN was used to infer user goals and needs by considering past program states and user actions sequences. Akiba and Tanaka used a BN to represent user models in dialog systems [1]. For the present approach a BN is used to store demographic information and to sample user models for AUE.…”

Section: Methodsmentioning

confidence: 99%

Demographic user characteristic sampling for model-based usability evaluation

Schulz

2014

Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational

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Using software for model-based usability evaluation is uncommon today, as the modelling process is considered as overhead to the actual design work. The aim of the present paper is to describe a concept, which may make modelbased usability evaluation more worthwhile and feasible. The concept is based on sampling user models based on demographic characteristics; these characteristics may help to estimate the severity of usability problems found with the help of the user model. To sample representative user models, a simple Bayesian network (BN) was constructed, holding information about age and gender distributions, and attitudes towards technology. The results of the simulation suggest that a BN is an appropriate tool to store user information for modelling purposes, and thus may improve model-based usability evaluation.

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Section: Methodsmentioning

confidence: 99%

Demographic user characteristic sampling for model-based usability evaluation

Schulz

2014

Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational

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“…Another step in the user modeling process is finding the best way to represent the content in the user model: using simple vectors or bags of words [1], property-value pairs [13], complex probabilistic approaches (such as bayesian networks [2]), or overlay over the domain model [11], where the user's current state (e.g. interest or knowledge) with respect to domain concepts is recorded.…”

Section: User Modelmentioning

confidence: 99%

Anisotropic propagation of user interests in ontology-based user models

Cena

Likavec

Osborne

2013

Information Sciences

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This work contributes to the development of ontology-based user models, devised as overlays over conceptual hierarchies derived from domain ontologies. We tackle the problem of propagation of user interests in such a conceptual hierarchy. In addition to accounting for the hierarchical structure of the domain and the type and amount of feedback provided by the user, the principal contributions introduced in this work are: (i) horizontal propagation which enables propagation among siblings, in addition to vertical propagation among ancestors and descendants; (ii) anisotropic vertical propagation which permits user interests to be propagated differently upward and downward; (iii) context-dependance which introduces the possibility to propagate differently according to various contexts for specific applications; (iv) support for dynamic ontology maintenance, i.e. preserving the user interest values when adding or removing a node from the conceptual hierarchy. Our approach supports finer recommendation modalities and contributes to the resolution of the cold start problem, since it allows for propagation from a small number of initial concepts to other related domain concepts by exploiting the conceptual hierarchy of the domain. A field evaluation confirmed the effectiveness of our approach w.r.t. the traditional vertical propagation.

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“…A Bayesian Network is an estimation model that represents the cause-and-effect relationships between several phenomena using probability. This method [7] is widely used in fields such as map acquisition for mobile robots [3,4], computer error diagnosis [5], and interactive systems [6].…”

Section: Using a Bayesian Networkmentioning

confidence: 99%

PEXIS: Probabilistic experience representation based adaptive interaction system for personal robots

Inamura

Inaba

Inoue

2004

Systems & Computers in Japan

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SUMMARYIn this paper the authors focus on the interaction between users and personal robots that can move in a real environment. When the creation of robots that can perform in an ordinary home or office is considered, it is difficult to imagine beforehand what kind of environment will be used, and so the approach in which developers embed environmental knowledge and strategies for autonomous movement fails. Thus, the authors propose an approach in which knowledge of the environment and the knowledge needed to move are acquired after development by allowing the robot and the user who is using the robot to engage in dialogue, and representing experience statistically. The authors introduce PEXIS, an interaction system developed using this approach, and then describe the characteristics and utility of their system via examples such as learning to avoid objects in an office environment and adapting to the vocabulary expressions of a particular user.

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A Bayesian approach for user modeling in dialogue systems

Cited by 23 publications

References 6 publications

Demographic user characteristic sampling for model-based usability evaluation

Demographic user characteristic sampling for model-based usability evaluation

Anisotropic propagation of user interests in ontology-based user models

PEXIS: Probabilistic experience representation based adaptive interaction system for personal robots

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