CLC number: TP311.5
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-09-04
Cited: 0
Clicked: 5315
Dan-yang Jiang, Hong-hui Chen. Cohort-based personalized query auto-completion[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1246-1258.
@article{title="Cohort-based personalized query auto-completion",
author="Dan-yang Jiang, Hong-hui Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="9",
pages="1246-1258",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800010"
}
%0 Journal Article
%T Cohort-based personalized query auto-completion
%A Dan-yang Jiang
%A Hong-hui Chen
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 9
%P 1246-1258
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800010
TY - JOUR
T1 - Cohort-based personalized query auto-completion
A1 - Dan-yang Jiang
A1 - Hong-hui Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 9
SP - 1246
EP - 1258
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800010
Abstract: query auto-completion (QAC) facilitates query formulation by predicting completions for given query prefix inputs. Most web search engines use behavioral signals to customize query completion lists for users. To be effective, such personalized QAC models rely on the access to sufficient context about each user’s interest and intentions. Hence, they often suffer from data sparseness problems. For this reason, we propose the construction and application of cohorts to address context sparsity and to enhance QAC personalization. We build an individual’s interest profile by learning his/her topic preferences through topic models and then aggregate users who share similar profiles. As conventional topic models are unable to automatically learn cohorts, we propose two cohort topic models that handle topic modeling and cohort discovery in the same framework. We present four cohort-based personalized QAC models that employ four different cohort discovery strategies. Our proposals use cohorts’ contextual information together with query frequency to rank completions. We perform extensive experiments on the publicly available AOL query log and compare the ranking effectiveness with that of models that discard cohort contexts. Experimental results suggest that our cohort-based personalized QAC models can solve the sparseness problem and yield significant relevance improvement over competitive baselines.
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