Predictive User Models Research Articles

A novel user satisfaction prediction model for future network provisioning

The notion of user perception has grown in terms of its importance and complexity. This paper presents results of an experimental study focused on predictive modeling of the relations between the user perception, user satisfaction and objective technical parameters in data communication services. A new model for prediction of user satisfaction was devised using probability theory based on Markov chain. Two experiments were completed for web browsing scenarios. The results of the first experiment have confirmed that previous user experience has significant effect on the user perception of quality and should represent a vital element of future predictive user models. The result of the second experiment is a user satisfaction prediction model, which presents a novel insight and deeper understanding of user perception of quality. This model can significantly improve level of user satisfaction with services in telecommunications systems if implemented within advanced system design, optimization and quality assurance procedures.

Personalised viewing-time prediction in museums

People are often overwhelmed by the large amount of information provided in museum spaces, which makes it difficult for them to select exhibits of potential interest. As a first step in recommending exhibits where a visitor may wish to spend some time, this article investigates predictive user models for personalised prediction of museum visitors' viewing times at exhibits. We consider two content-based models and a nearest-neighbour collaborative filter, and develop a collaborative model based on the theory of spatial processes which relies on a notion of distance between exhibits. We discuss models of exhibit distance derived from viewing-time similarity, semantic similarity and walking distance. The results from our evaluation with a real-world dataset of visitor pathways collected at Melbourne Museum (Melbourne, Australia) suggest that utilising walking and semantic distances between exhibits enables more accurate predictions of a visitor's viewing times of unseen exhibits than using distances derived from observed exhibit viewing times. Our results also show that all models outperform a non-personalised baseline, that content-based viewing time prediction yields better results than nearest-neighbour collaborative prediction, and that our collaborative model based on spatial processes attains the highest predictive accuracy overall.

Computational Modeling of User Errors for the Design of Virtual Scanning Keyboards

Virtual scanning keyboards are used by persons with severe speech and motion impairments as communication aids. Each of these systems consists of a virtual keyboard and a "scanning and access switch" based alternate input method. Designers of such keyboards face problems due to the difficulties in testing prototypes with disabled users. Model-based design approaches were proposed in order to alleviate the problems. In model-based design, systems are evaluated with user models reducing the need for extensive user testing. The existing model-based approaches, however, do not consider the effect of user errors in evaluating systems. The lack of consideration of errors limits the practical usefulness of the resulting designs. To overcome this limitation, we have performed empirical studies of errors on virtual scanning keyboards. From our study results, we have derived predictive models of user's error behavior. We have used the models to develop "ErrorProneness," a numerical error measure for virtual scanning keyboards. We have proposed a method using the "ErrorProneness" measure for taking the effects of errors into account in model-based design. Methods employed in our study, results obtained, the predictive user models, the error measure, and the proposed design method are presented in this paper.

Improving command and control speech recognition on mobile devices: using predictive user models for language modeling

Command and control (C&C) speech recognition allows users to interact with a system by speaking commands or asking questions restricted to a fixed grammar containing pre-defined phrases. Whereas C&C interaction has been commonplace in telephony and accessibility systems for many years, only recently have mobile devices had the memory and processing capacity to support client-side speech recognition. Given the personal nature of mobile devices, statistical models that can predict commands based in part on past user behavior hold promise for improving C&C recognition accuracy. For example, if a user calls a spouse at the end of every workday, the language model could be adapted to weight the spouse more than other contacts during that time. In this paper, we describe and assess statistical models learned from a large population of users for predicting the next user command of a commercial C&C application. We explain how these models were used for language modeling, and evaluate their performance in terms of task completion. The best performing model achieved a 26% relative reduction in error rate compared to the base system. Finally, we investigate the effects of personalization on performance at different learning rates via online updating of model parameters based on individual user data. Personalization significantly increased relative reduction in error rate by an additional 5%.