Capturing user intent for information retrieval

Abstract

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...

The evaluation of adaptive and personalised information retrieval systems: a review

“…Since the first introduction of the term in 2000, the scientific community has adopted this concept in planning and conducting empirical studies. The researcher acknowledge that many authors explicitly refer back to the foundational papers published on the topic to justify experimental designs, to provide rationale for goals or structure of their evaluation studies (Ortigosa and Carro, 2003;Gena, 2005;Goren-Bar et al, 2005;Petrelli and Not, 2005;Arruabarrena et al, 2006;Glahn et al, 2007;Kobsa, 2007;Nguyen and Santos Jr, 2007;Ley et al, 2009;Limongelli et al, 2008;Popescu, 2009;Santos and Boticario, 2009) or to demonstrate methodological shortcomings of existing studies (Masthoff, 2002;Gena, 2005;Brusilovsky et al, 2006;Yang and Huo, 2008;Brown et al, 2009). The fact that layered evaluation received such a high level of attention in the literature reaffirms the claim that the evaluation of adaptive systems implicates some inherent difficulties.…”

Modeling Users for Adaptive Information Retrieval by Capturing User Intent

“…We assess its effectiveness with an evaluation methodology which allows us to compare with the existing approaches from the IR community as well as validates its effectiveness with human subjects. This chapter brings together some of our past results and user modeling experiments providing a unified formal framework and evaluations with synthesized data sets and human testing (Santos et al, 2003a;Santos et al, 2003b;Nguyen et al, 2004a;Nguyen et al, 2004b;Nguyen, 2005). Uncertainty is one of the key challenges in modeling a user for IR, as mentioned earlier.…”

“…The difference of our approach versus the existing work in this direction is that we provide a learning capability for the system to discover new knowledge based on analyzing the documents relevant to the user and their context, i.e., why a user is focusing on the given information by exploring the structure of information instead of frequency. In our evaluation framework, we assess the effectiveness of our user model with regards to the target application in terms of its influence on retrieval performance as well as its effects on helping humans to retrieve more documents that are relevant to an individual's needs (Santos et al, 1999;Santos et al, 2003a;Santos et al, 2003b;Nguyen et al, 2004a;Nguyen et al, 2004b;Nguyen, 2005). We discuss the results of our evaluation on the effectiveness of our user model with regards to retrieval performance using the CRANFIELD, CACM, and MEDLINE collections (Salton & Buckley, 1990).…”

An Interaction Mining Approach for Classifying User Intent on the Web

“…For instance, e-commerce organizations might predict user needs, and advertise the products that users will most likely buy. Thus, multimedia catalogues, web and information retrieval systems need to embed search engines capable of capturing user intent, which is the focus of user intention understanding (UIU) research area [26].…”