User modeling

About: User modeling is a research topic. Over the lifetime, 10701 publications have been published within this topic receiving 278012 citations.

Adaptive user interface systems and methods for healthcare applications

Abstract: Systems and methods for providing adaptive, work-centered healthcare services via an adaptive user interface are provided. An example adaptive user interface apparatus includes a plurality of widgets providing applications and/or data to a user based on a particular data context, the plurality of widgets responsive to input from the user. The apparatus also includes a query engine providing customized query results from a connectivity framework of data sources based on a user query and the particular data context. The apparatus further includes a user interface display area configurable by the user to position widget(s) and query engine access to enable the user to access, input, and search medical information across a healthcare enterprise. The user interface includes an adaptive, work-centered interface employing an ontology modeling approach to characterize the user's workspace based on workflow activities and computation mechanisms to support the user's workflow and access to enterprise applications and data.

Context based customization of word assistance functions

Abstract: For generating customized word assistance functions based on user information and context, a system, apparatus, method, and computer program product are disclosed. The apparatus includes a processor and a memory that stores code executable by the processor, including code that accesses personal information of a user, identifies a dialectal nuance of the user based on the personal information, and selects a word recognition dictionary based on the dialectal nuance. The dialectal nuance may be based on a location of the user, a nationality of the user, an age of the user, an education level of the user, and/or a profession of the user. The apparatus may also suggest one or more text entries from the selected word recognition dictionary based on the user input.

Efficient model learning for dialog management

Abstract: Intelligent planning algorithms such as the Partially Observable Markov Decision Process (POMDP) have succeeded in dialog management applications [10, 11, 12] because they are robust to the inherent uncertainty of human interaction. Like all dialog planning systems, however, POMDPs require an accurate model of the user (e.g., what the user might say or want). POMDPs are generally specified using a large probabilistic model with many parameters. These parameters are difficult to specify from domain knowledge, and gathering enough data to estimate the parameters accurately a priori is expensive. In this paper, we take a Bayesian approach to learning the user model simultaneously with dialog manager policy. At the heart of our approach is an efficient incremental update algorithm that allows the dialog manager to replan just long enough to improve the current dialog policy given data from recent interactions. The update process has a relatively small computational cost, preventing long delays in the interaction. We are able to demonstrate a robust dialog manager that learns from interaction data, out-performing a hand-coded model in simulation and in a robotic wheelchair application.

Decentralized User Modeling with UserML and GUMO

Abstract: We present a new architecture for decentralized user modeling and briefly discuss the user model markup language USERML, the general user model ontology GUMO for the uniform interpretation of decentralized user models, and the integration of ubiquitous applications with the u2m.org user model service. The motivation is that ubiquitous evaluation of user behavior with a variety of systems in the web or the physical world might lead to attractive new services. 1 Approach and Architecture We developed the RDF-based user model exchange language UserML to enable decentralized systems to communicate over user models. The idea is to spread the information among all adaptive systems, either with a mobile device or via ubiquitous networks. UserML statements can be arranged and stored in distributed repositories in XML, RDF or SQL. Each mobile and stationary device has an own repository of situational statements, either local or global, dependent on the network accessability. A mobile device can perfectly be integrated via wireless lan or bluetooth into the intelligent environment, while a stationary device could be isolated without network access. The different applications or agents produce or use UserML statements Fig. 1. The syntax-semantics interplay between USERML and GUMO to represent the user model information. UserML forms the syntactic description in the knowledge exchange process, see figure 1. Each concept like the user model auxiliary 62 Dominik Heckmann et al. hasProperty and the user model dimension timePressure points to a semantical definition of this concept which is either defined in the general user model ontology GUMO, the UbisWorld ontology, which is specialized for ubiquitous computing, or the general SUMO/MILO ontology, see [1]. The merging of partial, decentralized user models is realized by combining the different user model repositories, while the inferential integration is done by filters and conflict resolution strategies as shown in figure 2(b). Figure 2(a) and figure 2(c) show the upward and downward inference from repositories or journals to the user model and vice versa.

User profiling for Web page filtering

Abstract: To help address pressing problems with information overload, researchers have developed personal agents to provide assistance to users in navigating the Web. To provide suggestions, such agents rely on user profiles representing interests and preferences, which makes acquiring and modeling interest categories a critical component in their design. Existing profiling approaches have only partially tackled the characteristics that distinguish user profiling from related tasks. The authors' technique generates readable user profiles that accurately capture interests, starting from observations of user behavior on the Web.