@inproceedings{1a667be96cd749d2b63f6464ab136385,
title = "Query recommendation considering search performance of related queries",
abstract = "In this paper, we propose a new query recommendation method. This method is designed to generate recommended queries which are not only related to input query, but also provide high quality search results to users. Existing query recommendation methods are mostly focused on users' intention or the relationship between input query andrecommended queries.Because the limitation of Web resource and search engine's index, not all recommended queries lead to good search results. Such recommendation will not help users to find the information they need. In our work, we use machine learning methods to re-rank a pre-generated recommendation candidate list. We select some user behavior features to filter out the queries which have poor search performance. The experiment results show that our method can recommend queries which are related and provide useful results to users.",
keywords = "Query Recommendation, User Behavior, User Experience",
author = "Yufei Xue and Yiqun Liu and Tong Zhu and Min Zhang and Shaoping Ma and Liyun Ru",
year = "2010",
doi = "10.1007/978-3-642-17187-1\_40",
language = "英语",
isbn = "3642171869",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "410--419",
booktitle = "Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings",
note = "6th Asia Information Retrieval Societies Conference, AIRS 2010 ; Conference date: 01-12-2010 Through 03-12-2010",
}