Abstract
During a search task, a user’s search intention is possible inaccurate. Even with clear information need, it is probable that the search query cannot precisely describe the user’s need. And besides, the user is utterly impossible browse all the returned results. Thus, a selected and valuable returned search list is quite important for a search system. Actually, there are lots of reliable and highly relevant personal documents existing in a user’s personal computer. Based on the desktop documents, it is relevantly easy to understand the user’s current knowledge level about the present search subject, which is useful to predict a user’s need. An approach was proposed to exploit the potential of desktop context to refine the search returned list. Firstly, to attain a comprehensive long-term user model, the operational history and a series of time-related information were analyzed to achieve the attention degree that a user paid to a document. And the keywords and user tags were focused on to understand the content. Secondly, working scenario was regarded as the most valuable information to construct a short-term user model, which directly suggested what exactly a user was working on. Experiment results showed that desktop context could effectively help refine the search returned results, and only the effectively combination of the long-term user model and the short-term user model could offer more relevant items to satisfy the user.
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Li, X., Yu, Y., Ouyang, C. (2015). Refine Search Results Based on Desktop Context. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_18
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DOI: https://doi.org/10.1007/978-3-319-25207-0_18
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