Selected new training documents to update user profile
Data(s) |
2010
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Resumo |
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. |
Identificador | |
Publicador |
ACM |
Relação |
DOI:10.1145/1871437.1871540 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. http://purl.org/au-research/grants/ARC/DP0988007 |
Direitos |
Copyright 2010 ACM |
Fonte |
Faculty of Science and Technology |
Palavras-Chave | #080600 INFORMATION SYSTEMS #Relevance Feedback #retrieval accuracy #Adaptive information filtering (AIF) #Adaptive Relevance Features Discovery (ARFD) |
Tipo |
Conference Paper |