User modeling

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

User support apparatus and system using agents

Abstract: A user support system employing agent technology is provided. The entrance server identifies the user command by matching it with a collection of anticipated user commands. An index search is performed to identify the contents of the user command. The identified user command is then used to determine which specialized server should respond to the user command. The specialized server contains a collection of action patterns for an agent to use in responding to a user command. An agent supports a user in searching for information and/or navigating to the desired information through friendly conversation with that user. The entrance server is configured as a portal site and a specialized server is provided for each specialized field.

Adaptive HyperText and Hypermedia

Abstract: Preface. 1. Methods and Techniques of Adaptive Hypermedia P. Brusilovsky. 2. Adaptive Hypertext Navigation Based on User Goals and Context C. Kaplan, et al. 3. Metadoc: An Adaptive Hypertext Reading System C. Boyle, A.O. Encarnacion. 4. User Modelling in the Interactive Anatomy Tutoring System ANATOM-TUTOR I.H. Beaumont. 5. Hypadapter: An Adaptive Hypertext System for Exploratory Learning and Programming H. Hohl, et al. 6. A Glass Box Approach to Adaptive Hypermedia K. Hook, et al. 7. User-Centered Indexing for Adaptive Information Access N. Mathe, J.R. Chen. 8. A Task-Centred Approach for User Modeling a Hypermedia Office Documentation System J. Vassileva. Index.

Towards Time-Dependant Recommendation based on Implicit Feedback

Abstract: Context-aware recommender systems (CARS) aim at improving users’ satisfaction by tailoring recommendations to each particular context. In this work we propose a contextual pre-ltering technique based on implicit user feedback. We introduce a new context-aware recommendation approach called user micro-proling . We split each single user prole into several possibly overlapping sub-proles, each representing users in particular contexts. The predictions are done using these micro-proles instead of a single user model. The users’ taste can depend on the exact partition of the contextual variable. The identication of a meaningful partition of the users’ prole and its evaluation is a non-trivial task, especially when using implicit feedback and a continuous contextual domain. We propose an o-line evaluation procedure for CARS in these conditions and evaluate our approach on a time-aware music recommendation sytem.

Using predictive user models for language modeling on a personal device with user behavior models based on statistical modeling

Abstract: A system and method for prediction of a user goal for command/control of a personal device (e.g., mobile phone) is provided. The system employs statistical model(s) that can predict a command based, at least in part, on past user behavior (e.g., probability distribution over a set of predicates, and, optionally arguments). Further, the system can be employed with a speech recognition component to facilitate language modeling for predicting the user goal. The system can include predictive user models (e.g., predicate model and argument model) that receive a user input (e.g., utterance) and employ statistical modeling to determine the likely command without regard to the actual content of the input (e.g., utterance). The system employs features for predicting the next user goal which can be stored in a user data store. Features can capture personal idiosyncrasies or systematic patterns of usage (e.g., device-related, time-related, predicate-related, contact-specific and/or periodic features).

Method and apparatus for utility-directed prefetching of web pages into local cache using continual computation and user models

Abstract: A technique that, through continual computation, harnesses available computer resources during periods of low processing activity and low network activity, such as idle time, for prefetching, e.g., web pages, or pre-selected portions thereof, into local cache of a client computer. This technique utilizes a probabilistic user model to specify, at any one time, those pages or portions of pages that are likely to be prefetched given, e.g., a web page currently being rendered to a user, which promise to provide the largest benefit (expected utility) to the user. Specifically, once a user, at a client computer, enters an address of a desired web page, a set containing web addresses of web pages, that based on the user model are each likely to be accessed next by that user, are determined, with corresponding files therefor prefetched, in order of their expected utility to the user, by the client computer during intervals of low processing activity and low network activity. Expected utility of a page or portion is assessed as a product of rate of refinement in utility of that page or portion to the user multiplied by its transition probability. Once prefetched, these pages or portions are stored in local cache at the client computer for ready access should the user next select any such page or portion.