Paper Info
Title
Refine Search Results Based on Desktop Context.
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.
Year
DOI
Venue
2015
10.1007/978-3-319-25207-0_18
NLPCC
Keywords
DocType
Volume
Desktop context,Information retrieval,User model,Personal information space
Conference
9362
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
15
3
Name
Order
Citations
PageRank
Xiaoyun Li100.68
Ying Yu200.34
Chunping Ouyang363.35