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.
Supported by Natural Science Foundation (60736044, 60903107) and Research Fund for the Doctoral Program of Higher Education of China (20090002120005).
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Xue, Y., Liu, Y., Zhu, T., Zhang, M., Ma, S., Ru, L. (2010). Query Recommendation Considering Search Performance of Related Queries. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_40
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DOI: https://doi.org/10.1007/978-3-642-17187-1_40
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