@inproceedings{de5f50cae1484a189ecc80ee5423bd02,
title = "Mining long-term search history to improve search accuracy",
abstract = "Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.",
keywords = "Context, Query expansion, Search history",
author = "Bin Tan and Xuehua Shen and Zhai, {Cheng Xiang}",
year = "2006",
doi = "10.1145/1150402.1150493",
language = "English (US)",
isbn = "1595933395",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "718--723",
booktitle = "KDD 2006",
address = "United States",
note = "KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; Conference date: 20-08-2006 Through 23-08-2006",
}