No abstract

User modelling is a key element in successfully assisting intelligence analysts who must gather information and make decisions without being overloaded by the massive amounts of data available on a daily basis most of which are irrelevant. Furthermore, with user modelling, we can predict the goals and intentions of the analyst in order to better serve their information seeking tasks by providing better re-organization and presentation of data as well as pro-actively retrieve novel and relevant information as it arises. Our goal is to provide a dynamic user model of an analyst and work with him as he goes about his daily tasks.

We study the problem of employing a cognitive user model for information retrieval in which knowledge about a user is captured and used for improving retrieval performance and user satisfaction. In this proposed research, we improve retrieval performance and user satisfaction for information retrieval by building a user model to capture user intent dynamically through analyzing behavioral information from retrieved relevant documents, and by combining captured user intent with the elements of an information retrieval system. We use decision theoretic principles and bayesian networks for building this model. The novelties of our approach lie with the fine-grained representation of the model, the ability to learn user knowledge incrementally and dynamically, the integration of user intent and system elements for improving retrieval performance and the unified evaluation framework to assess the accuracy of user intent captured and effectiveness of our model. Problem StatementWe study the problem of employing a cognitive user model for information retrieval in which knowledge about a user is captured and used for improving retrieval performance and user satisfaction. The problem of employing a user model for information retrieval has been investigated since the late 80s (Brajnik, Guida, & Tasso 1987) to address the difficulty of traditional information retrieval which is to satisfy a user's information needs by retrieving information with good quality quickly. It has many applications from information filtering, and text recommendation systems (e.g (Balabanovic 1998;Billsus & Pazzani 2000)). This problem is wellknown for the challenges it poses to Information Retrieval (IR), and User Modeling (UM) communities. We have identified four primary challenges of this problem which are (i) non-observability of user knowledge (ii) uncertainty in modeling user interactions (ii) vagueness of a user's information needs (iv) and dynamics of user knowledge which changes over time as a result of new information, and temporal factors. These challenges come from the main problem of modeling a user in information seeking. Unfortunately, traditional IR does not offer a way to overcome these challenges because its framework supported very little users' involvement (Saracevic 1996). The thesis of this proposed research is to improve retrieval performance and user satisfaction for IR by building a user model to capture user intent dynamically through analyzing behavioral information of retrieved relevant documents, and combine it with the elements of an IR system. Our methodologies for this research are (i) to combine system-centered approaches and user-centered approaches by taking advantage of well-established evaluation frameworks in IR, and using the strength of knowledge representation techniques in artificial intelligence (AI) to build this model. As pointed out in (Saracevic, Spink, & Wu 1997), so far there has been a little crossover between IR and AI communities with regards to building user models for IR; and (ii) to cr...

Intelligent foraging, gathering and matching (I-FGM) has been shown to be an effective tool for intelligence analysts who have to deal with large and dynamic search spaces. I-FGM introduced a unique resource allocation strategy based on a partial information processing paradigm which, along with a modular system architecture, makes it a truly novel and comprehensive solution to information retrieval in such search spaces. This paper provides further validation of its performance by studying its behavior while working with highly dynamic databases. Results from earlier experiments were analyzed and important changes have been made in the system parameters to deal with dynamism in the search space. These changes also help in our goal of providing relevant search results quickly and with minimum wastage of computational resources. Experiments have been conducted on I-FGM in a realistic and dynamic simulation environment, and its results are compared with two other control systems. I-FGM clearly outperforms the control systems.