Context-sensitive information retrieval using implicit feedback
X. Shen, B. Tan, und C. Zhai. Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, Seite 43--50. New York, NY, USA, ACM, (2005)
DOI: 10.1145/1076034.1076045
Zusammenfassung
A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.
%0 Conference Paper
%1 citeulike:776870
%A Shen, Xuehua
%A Tan, Bin
%A Zhai, ChengXiang
%B Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
%C New York, NY, USA
%D 2005
%I ACM
%K adaptive-search implicit-feedback information-retrieval
%P 43--50
%R 10.1145/1076034.1076045
%T Context-sensitive information retrieval using implicit feedback
%U http://dx.doi.org/10.1145/1076034.1076045
%X A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.
%@ 1-59593-034-5
@inproceedings{citeulike:776870,
abstract = {{A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.}},
added-at = {2017-11-15T17:02:25.000+0100},
address = {New York, NY, USA},
author = {Shen, Xuehua and Tan, Bin and Zhai, ChengXiang},
biburl = {https://www.bibsonomy.org/bibtex/227260da9b3385221ce4119e1a8f55f14/brusilovsky},
booktitle = {Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval},
citeulike-article-id = {776870},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1076045},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/1076034.1076045},
doi = {10.1145/1076034.1076045},
interhash = {408812345d5346efca26f5c6ec1257da},
intrahash = {27260da9b3385221ce4119e1a8f55f14},
isbn = {1-59593-034-5},
keywords = {adaptive-search implicit-feedback information-retrieval},
location = {Salvador, Brazil},
pages = {43--50},
posted-at = {2009-04-01 04:53:15},
priority = {3},
publisher = {ACM},
series = {SIGIR '05},
timestamp = {2019-11-19T02:52:47.000+0100},
title = {{Context-sensitive information retrieval using implicit feedback}},
url = {http://dx.doi.org/10.1145/1076034.1076045},
year = 2005
}