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Selected new training documents to update user profile

Algarni, Abdulmohsen, Li, Yuefeng, & Xu, Yue (2010) Selected new training documents to update user profile. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, ACM, Fairmont Royal York, Toronto, pp. 799-808.

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Abstract

Relevance Feedback (RF) has been proven very effective for improving retrieval accuracy. Adaptive information filtering (AIF) technology has benefited from the improvements achieved in all the tasks involved over the last decades. A difficult problem in AIF has been how to update the system with new feedback efficiently and effectively. In current feedback methods, the updating processes focus on updating system parameters. In this paper, we developed a new approach, the Adaptive Relevance Features Discovery (ARFD). It automatically updates the system's knowledge based on a sliding window over positive and negative feedback to solve a nonmonotonic problem efficiently. Some of the new training documents will be selected using the knowledge that the system currently obtained. Then, specific features will be extracted from selected training documents. Different methods have been used to merge and revise the weights of features in a vector space. The new model is designed for Relevance Features Discovery (RFD), a pattern mining based approach, which uses negative relevance feedback to improve the quality of extracted features from positive feedback. Learning algorithms are also proposed to implement this approach on Reuters Corpus Volume 1 and TREC topics. Experiments show that the proposed approach can work efficiently and achieves the encouragement performance.

Impact and interest:

1 citations in Scopus
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ID Code: 42083
Item Type: Conference Paper
Keywords: Relevance Feedback, retrieval accuracy, Adaptive information filtering (AIF), Adaptive Relevance Features Discovery (ARFD)
DOI: 10.1145/1871437.1871540
ISBN: 9781450300995
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 ACM
Deposited On: 22 Jun 2011 13:10
Last Modified: 01 Mar 2012 00:29

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