NDLI: Identifying users' topical tasks in web search

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Identifying users' topical tasks in web search

Content Provider	ACM Digital Library
Author	Song, Yangqiu Wang, Haixun Zhou, Xiaofang Hua, Wen
Abstract	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.
Starting Page	93
Ending Page	102
Page Count	10
File Format	PDF
ISBN	9781450318693
DOI	10.1145/2433396.2433410
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-02-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Interleaved task Task identification Conceptualization Probase
Content Type	Text
Resource Type	Article

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