Use Of User Models Research Articles

Towards standardisation of user models for simulation and adaptation purposes

The use of user models can be very valuable when trying to develop accessible and ergonomic products and services taking into account users' specific needs and preferences. Simulation of user---product interaction using user models may reveal accessibility issues at the early stages of design and development, and this results to a significant reduction in costs and development time. Moreover, user models can be used in adaptive interfaces enabling the personalised customisation of user interfaces that enhances the accessibility and usability of products and services. This paper presents the efforts of the Virtual User Modelling and Simulation Standardisation `VUMS' cluster of projects towards the development of an interoperable user model, able to describe both able-bodied and people with various kinds of disabilities. The VUMS cluster is consisted by the VERITAS, MyUI, GUIDE, and VICON FP7 European projects, all involved in user modelling from different perspectives. The main goal of the VUMS cluster was the development of a unified user model that could be used by all the participant projects and that could be the basis of a new user model standard. Currently, within the VUMS cluster, a common user model has been defined and converters that enable the transformation from each project's specific user model to the VUMS user model and vice versa have been developed enabling, thus, the exchange of user models between the projects.

Generation and evaluation of user tailored responses in multimodal dialogue

When people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners’ comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving higher level goals such as changing the listener’s beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice-giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi-attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness.

Generation and evaluation of user tailored responses in multimodal dialogue

AbstractWhen people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners' comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving higher level goals such as changing the listener's beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice‐giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi‐attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness.

User Models and Filtering Agents for Improved Internet Information Retrieval

Over the past few years, the amount of electronic information available through the Internet has increased dramatically. Unfortunately, the search tools currently available for retrieving and filtering information in this space are not effective in balancing relevance and comprehensiveness. This paper analyzes the results of experiments in which HTML documents are searched with user models and software agents used as intermediaries to the search. Simple user models are first combined with search specifications (or ’User Needs‘), to define an Enhanced User Need. Then Uniform Resource Agents are constructed to filter information based on the EUN parameters. The results of searches using different agents are then compared to those obtained through a comparable simple keyword search, and it is shown that a user searching a pool of existing agents can obtain better search results than by conducting a traditional keyword search. This work thus demonstrates that the use of user models and information filtering agents do improve search results and may be used to improve Internet information retrieval.