A probabilistic topic-based ranking framework for location-sensitive domain information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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Users use a few keywords to post queries to search engines. Search engines, often, fail to return answers that their users seek because the keyword queries incompletely specify the information being sought and because of the ambiguity of natural language terms. Query expansion, where additional keywords are added automatically or semi-automatically to the user's query before it is run, has been used to improve the accuracy of search engines. We propose a framework where first, we identify whether a query should be expanded based on its features. We focus on identifying queries whose results are location-sensitive and expand them using keywords from similar queries from similar locations. Similarity between queries is derived using a novel LDA-based topic-level query similarity measure. We conducted experiments with query log data from the CiteSeer digital library and see a small improvement of results due to our query expansion.