Capturing User Intent for Analytic Process

Enabling a Collaborative Problem- Solving Framework through User Intent Modeling of the Analytic Process

Capturing User Intent for Information Retrieval

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 infor...

An Evaluation of the Accuracy of Capturing User Intent for Information Retrieval

This paper reports our evaluation of the accuracy of capturing a user’s intent in an informationseeking task. Specifically, we would like to assess how accurately a user’s short-term goals, methods, and context in an information seeking task have been captured. Our method is to compare a machine-generated model against a human-generated model with 5 users using the CACM collection. Our results ...

Hybrid User Model for Information Retrieval

In this paper, we report our development of a hybrid user model for improving a user’s effectiveness in a search. Specifically, we dynamically capture a user’s intent and combine the captured user intent with the elements of an information retrieval system in a decision theoretic framework. Our solution is to identify a set of key attributes describing a user’s intent, and determine the interac...

A Concept-based Retrieval Method for Entity-oriented Search

Entity-oriented retrieval aims to return a list of relevant entities rather than documents to provide exact answers for user queries. The nature of entity-oriented retrieval requires identifying the semantic intent of user queries, i.e., understanding the semantic role of query terms and determining the semantic categories which indicate the class of target entities. Existing methods are not ab...