User modeling

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

Method and system for modifying the behavior of an application based upon the application's grammar

Abstract: A voice application platform receives information, such as a grammar and/or a prompt, from an application, which is indicative of the response(s) that the application expects. The voice application platform modifies the way the user can interact with the user interface and the application as a function of the expected responses. The voice application platform can provide a more consistent user interface by enabling the user to use terms or commands that the user is familiar with to interact with the application and the voice application platform performs the conversion between the user response and the response expected by the application in a manner transparent to the user and the application. The voice application platform can store information about the user and provide the appropriate information to the application (as requested) automatically based upon prior authorization from the user or by the voice application platform prompting the user on an as necessary basis. The voice application platform can also provide contextually based added functionality that is apparent or transparent to the user, for example, help for the user interface commands and help for the remote application.

Contextual task recommendation system and method for determining user's context and suggesting tasks

Abstract: A system and method in a network of devices, which ascertains the user's context to suggest the most preferred task for the user to perform. User's context includes his/her location, the content he/she is interested in, the devices which are being used by him/her, and the devices and content allowed to be used by the user. Using this context, the system suggests tasks to the user that are appropriate to that context. The determination of context and the suggestion of tasks are performed by keeping track of the content and the devices available to the user at any given time. The suggestion of tasks is performed in accordance with certain policies. For example, the user can control the behaviour of the system by changing the rules in the policy. Tasks are in the form of simple sentences that can be easily understood by the user. If, based on certain context, more than one task is possible, the system suggests the task with the highest priority to the user.

Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System

Abstract: Many e-commerce Web sites offer numerous services, so a product search could return an overwhelming set of options. Without system support, filtering irrelevant products, comparing alternatives, and selecting the best option can be difficult or impossible - especially for users connecting to the Web through a mobile device. Few Web-based recommender systems have been designed for mobile users. A critique-based recommendation methodology aids the acquisition and revision of user preferences in a mobile recommender system. We designed a product recommendation methodology and implemented it in MobyRek, a mobile-phone recommender system that helps users search for travel products. MobyRek supports limited asking and answering of questions and is based mostly on critiques.

Communications network interface for user friendly interactive access to online services

Abstract: A method and a system for connecting a service provider and a user at a remote location relative to the service provider, via a network based telecommunications device, are provided. The method includes utilizing a network operable terminal for transmitting communications between the service provider and the user, employing a display screen depicting a user selectable options menu, corresponding with service functions offered by the service provider, and enabling the user to choose an option from the user selectable options menu to initiate a corresponding communication to the service provider. The system includes a user operable terminal including a user interface display screen with user selectable menu options that are changeable in accordance with differing modes of operation, an internal processing unit configured for providing at least one selectable menu option; and a gateway service platform configured for transmitting an option selected from the at least one selectable menu option to and from one of the user or the provider.

Adapting to User Interest Drift for POI Recommendation

Abstract: Point-of-Interest recommendation is an essential means to help people discover attractive locations, especially when people travel out of town or to unfamiliar regions. While a growing line of research has focused on modeling user geographical preferences for POI recommendation, they ignore the phenomenon of user interest drift across geographical regions, i.e., users tend to have different interests when they travel in different regions, which discounts the recommendation quality of existing methods, especially for out-of-town users. In this paper, we propose a latent class probabilistic generative model Spatial-Temporal LDA (ST-LDA) to learn region-dependent personal interests according to the contents of their checked-in POIs at each region. As the users’ check-in records left in the out-of-town regions are extremely sparse, ST-LDA incorporates the crowd’s preferences by considering the public’s visiting behaviors at the target region. To further alleviate the issue of data sparsity, a social-spatial collective inference framework is built on ST-LDA to enhance the inference of region-dependent personal interests by effectively exploiting the social and spatial correlation information. Besides, based on ST-LDA, we design an effective attribute pruning (AP) algorithm to overcome the curse of dimensionality and support fast online recommendation for large-scale POI data. Extensive experiments have been conducted to evaluate the performance of our ST-LDA model on two real-world and large-scale datasets. The experimental results demonstrate the superiority of ST-LDA and AP, compared with the state-of-the-art competing methods, by making more effective and efficient mobile recommendations.