Mining and ranking users’ intents behind queries
How to understand intents behind user queries is crucial towards improving the performance of Web search systems. NTCIR-11 IMine task focuses on this problem. In this paper, we address the NTCIR-11 IMine task with two phases referred to as Query Intent Mining ( QIM ) and Query Intent Ranking ( QIR )...
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Published in | Information retrieval (Boston) Vol. 18; no. 6; pp. 504 - 529 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Dordrecht
Springer Netherlands
01.12.2015
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | How to understand intents behind user queries is crucial towards improving the performance of Web search systems. NTCIR-11 IMine task focuses on this problem. In this paper, we address the NTCIR-11 IMine task with two phases referred to as
Query Intent Mining
(
QIM
) and
Query Intent Ranking
(
QIR
). (I)
QIM
is intended to mine users’ potential intents by clustering short text fragments related to the given query. (II)
QIR
focuses on ranking those mined intents in a proper way. Two challenges exist in handling these tasks. (II) How to precisely estimate the intent similarity between user queries which only consist of a few words. (2) How to properly rank intents in terms of multiple factors, e.g. relevance, diversity, intent drift and so on. For the first challenge, we first investigate two interesting phenomena by analyzing query logs and document datasets, namely “
Same-Intent-Co-Click
” (
SICC
) and “
Same-Intent-Similar-Rank
” (
SISR
).
SICC
means that when users issue different queries, these queries represent the same intent if they click on the same URL.
SISR
means that if two queries denote the same intent, we should get similar search results when issuing them to a search engine. Then, we propose similarity functions for
QIM
based on the two phenomena. For the second challenge, we propose a novel intent ranking model which considers multiple factors as a whole. We perform extensive experiments and an interesting case study on the Chinese dataset of NTCIR-11 IMine task. Experimental results demonstrate the effectiveness of our proposed approaches in terms of both
QIM
and
QIR
. |
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ISSN: | 1386-4564 1573-7659 |
DOI: | 10.1007/s10791-015-9271-1 |