Modeling Users for Adaptive Information Retrieval by Capturing User Intent

Modeling Users for Adaptive Information Retrieval by Capturing User Intent

Eugene Santos Jr. (Dartmouth College, USA) and Hien Nguyen (University of Wisconsin - Whitewater, USA)
DOI: 10.4018/978-1-60566-306-7.ch005
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Abstract

In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a user’s intent is captured and used for improving his/her effectiveness in an information seeking task. The user intent is captured by analyzing the commonality of the retrieved relevant documents. The effectiveness of our user model is evaluated with regards to retrieval performance using an evaluation methodology which allows us to compare with the existing approaches from the information retrieval community while assessing the new features offered by our user model. We compare our approach with the Ide dec-hi approach using term frequency inverted document frequency weighting which is considered to be the best traditional approach to relevance feedback. We use CRANFIELD, CACM and MEDLINE collections which are very popular collections from the information retrieval community to evaluate relevance feedback techniques. The results show that our approach performs better in the initial runs and works competitively with Ide dec-hi in the feedback runs. Additionally, we evaluate the effects of our user modeling approach with human analysts. The results show that our approach retrieves more relevant documents to a specific analyst compared to keyword-based information retrieval application called Verity Query Language.
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Introduction

We studied the problem of employing a user model for Information Retrieval (IR) in which knowledge about a user is captured and used for improving a user’s performance. A user model addresses the “one size fits all” problem of the traditional IR system (Brusilovsky & Tasso, 2004). It takes into consideration a user’s knowledge, preferences, interests, and goals of using an IR system to deliver corresponding documents that are relevant to an individual and to present different parts of the same documents to a user according to his/her preferred ways of perceiving information. Modeling a user in an information seeking task also addresses the gap between what a user thinks as relevant versus what an IR system assumes that any user would think as relevant (Saracevic et al., 1997). The main purpose of user modeling for IR is to determine what the user intends to do within a system’s environment for the purpose of assisting the user to work more effectively and efficiently (Brown, 1998). The common approach for an IR application that employs a user model usually consists of two main steps: (i) to construct a static, or a dynamic user profile; and (ii) to adapt the target IR application to the user’s profile. An example of a static user profile is his/her demographic data such as gender, age, profession, and zip code. An example of a dynamic user profile is his domain knowledge, goals, and preferences. The first step is referred to as elicitation and the second step is referred to as adaptation. Elicitation of user models is a knowledge acquisition process. It is well-known in the artificial intelligence (AI) community that knowledge acquisition is the bottleneck of intelligent system design (Murray, 1997). Determining when and how to elicit the user’s knowledge is a domain and application-dependent decision. Adaptation involves how to retrieve documents that are relevant to the user’s profile and how to present these relevant documents according to the user’s preferred ways of perceiving information.

User modeling techniques have been used to improve a user’s performance in information seeking since the late 80s (examples of some early works are (Allen, 1990; Brajnik et al., 1987; Saracevic et al., 1997)). Modeling a user for information seeking poses many challenges to both the information retrieval and the user modeling communities. We have identified five main challenges as follows:

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Table of Contents
Foreword
Bamshad Mobasher
Acknowledgment
Max Chevalier, Christine Julien, Chantal Soule-Dupuy
Chapter 1
Laurent Candillier, Kris Jack, Françoise Fessant, Frank Meyer
The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative... Sample PDF
State-of-the-Art Recommender Systems
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Chapter 2
Neal Lathia
Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people... Sample PDF
Computing Recommendations with Collaborative Filtering
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Chapter 3
Edwin Simpson, Mark H. Butler
The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of... Sample PDF
Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling
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Chapter 4
Adaptive User Profiles  (pages 65-87)
Steve Cayzer, Elke Michlmayr
A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant... Sample PDF
Adaptive User Profiles
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Chapter 5
Eugene Santos Jr., Hien Nguyen
In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a... Sample PDF
Modeling Users for Adaptive Information Retrieval by Capturing User Intent
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Chapter 6
Mihaela Brut, Florence Sedes, Corinne Zayani
Inside the e-learning platforms, it is important to manage the user competencies profile and to recommend to each user the most suitable documents... Sample PDF
Ontology-Based User Competencies Modeling for E-Learning Recommender Systems
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Chapter 7
Colum Foley, Alan F. Smeaton, Gareth J.F. Jones
Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an... Sample PDF
Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment
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Chapter 8
Charles Delalonde, Eddie Soulier
This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The... Sample PDF
DemonD: A Social Search Engine Built Upon the Actor-Network Theory
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Chapter 9
Hager Karoui
In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference... Sample PDF
COBRAS: Cooperative CBR Bibliographic Recommender System
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Chapter 10
Zehra Cataltepe, Berna Altinel
As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative... Sample PDF
Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity
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Chapter 11
Nima Taghipour, Ahmad Kardan
Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender... Sample PDF
Web Content Recommendation Methods Based on Reinforcement Learning
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Chapter 12
Angela Carrillo-Ramos, Manuele Kirsch Pinheiro, Marlène Villanova-Oliver, Jérôme Gensel, Yolande Berbers
The authors of this chapter present a two-fold approach for adapting content information delivered to a group of mobile users. This approach is... Sample PDF
Collaborating Agents for Adaptation to Mobile Users
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Chapter 13
Cristina Gena, Liliana Ardissono
This chapter describes the user-centered design approach we adopted in the development and evaluation of an adaptive Web site. The development of... Sample PDF
A User-Centered Approach to the Retrieval of Information in an Adaptive Web Site
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Chapter 14
Antonella Carbonaro, Rodolfo Ferrini
Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently... Sample PDF
Personalized Information Retrieval in a Semantic-Based Learning Environment
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Chapter 15
Hanh Huu Hoang, Tho Manh Nguyen, A Min Tjoa
Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding... Sample PDF
A Semantic Web Based Approach for Context-Aware User Query Formulation and Information Retrieval
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About the Contributors