Identifying users' topical tasks in web search

Hua, Wen, Song, Yangqiu, Wang, Haixun and Zhou, Xiaofang (2013). Identifying users' topical tasks in web search. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM 2013. 6th ACM International Conference on Web Search and Data Mining, WSDM 2013, Rome, Italy, (93-102). 4 - 8 February 2013. doi:10.1145/2433396.2433410

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Author Hua, Wen
Song, Yangqiu
Wang, Haixun
Zhou, Xiaofang
Title of paper Identifying users' topical tasks in web search
Conference name 6th ACM International Conference on Web Search and Data Mining, WSDM 2013
Conference location Rome, Italy
Conference dates 4 - 8 February 2013
Proceedings title Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM 2013
Place of Publication New York, NY United States
Publisher ACM
Publication Year 2013
Sub-type Fully published paper
DOI 10.1145/2433396.2433410
Open Access Status
ISBN 9781450318693
145031869X
Start page 93
End page 102
Total pages 10
Collection year 2014
Language eng
Abstract/Summary A search task represents an atomic information need of a user in web search. Tasks consist of queries and their reformulations, and identifying tasks is important for search engines since they provide valuable information for determining user satisfaction with search results, predicting user search intent, and suggesting queries to the user. Traditional approaches to identifying tasks exploit either temporal or lexical features of queries. However, many query refinements are topical, which means that a query and its refinements may not be similar on the lexical level. Furthermore, multiple tasks in the same search session may interleave, which means we cannot simply order the searches by their timestamps and divide the session into multiple tasks. Thus, in order to identify tasks correctly, we need to be able to compare two queries at the semantic level. In this paper, we use a knowledgebase known as Probase to infer the conceptual meanings of queries, and automatically identify the topical query refinements in the tasks. Experimental results on real search log data demonstrate that Probase can indeed help estimate the topical affinity between queries, and thus enable us to merge queries that are topically related but dissimilar at the lexical level.
Subjects 1705 Computer Networks and Communications
1706 Computer Science Applications
Keyword Conceptualization
Interleaved task
Probase
Task identification
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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