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 Jun Miao

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Bibliometrics: publication history
Average citations per article8.00
Citation Count32
Publication count4
Publication years2012-2016
Available for download3
Average downloads per article347.00
Downloads (cumulative)1,041
Downloads (12 Months)202
Downloads (6 Weeks)11
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4 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
August 2016 ACM Transactions on Information Systems (TOIS): Volume 34 Issue 4, September 2016
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 8,   Downloads (12 Months): 144,   Downloads (Overall): 216

Full text available: PDFPDF
Traditional pseudo relevance feedback (PRF) models choose top k feedback documents for query expansion and treat those documents equally. When k is determined, feedback terms are selected without considering the reliability of these documents for relevance. Because the performance of PRF is sensitive to the selection of feedback terms, noisy ...
Keywords: Pseudo relevance feedback, text mining, topic modeling

2
March 2013 Information Processing and Management: an International Journal: Volume 49 Issue 2, March, 2013
Publisher: Pergamon Press, Inc.
Bibliometrics:
Citation Count: 4

The quality of feedback documents is crucial to the effectiveness of query expansion (QE) in ad hoc retrieval. Recently, machine learning methods have been adopted to tackle this issue by training classifiers from feedback documents. However, the lack of proper training data has prevented these methods from selecting good feedback ...
Keywords: Relevance feedback, Co-training, Query expansion

3 published by ACM
August 2012 SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 5
Downloads (6 Weeks): 1,   Downloads (12 Months): 18,   Downloads (Overall): 289

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Many information retrieval (IR) techniques have been proposed to improve the performance, and some combinations of these techniques has been demonstrated to be effective. However, how to effectively combine them is largely unexplored. It is possible that a method reduces the positive influence of the other one even if both ...
Keywords: hybrid model, rocchio's relevance feedback

4 published by ACM
August 2012 SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 23
Downloads (6 Weeks): 2,   Downloads (12 Months): 40,   Downloads (Overall): 536

Full text available: PDFPDF
Rocchio's relevance feedback model is a classic query expansion method and it has been shown to be effective in boosting information retrieval performance. The selection of expansion terms in this method, however, does not take into account the relationship between the candidate terms and the query terms (e.g., term proximity). ...
Keywords: rocchio's model, proximity- based term frequency, query expansion, pseudo relevance feedback



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