Topic based query suggestions for video search

Query suggestion is an assistive technology mechanism commonly used in search engines to enable a user to formulate their search queries by predicting or completing the next few query words that the user is likely to type. In most implementations, the suggestions are mined from query log and use som...

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Main Authors: WAN, Kong-Wah, TAN, Ah-hwee, LIM, Joo-Hwee, CHIA, Liang-Tien
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/6746
https://ink.library.smu.edu.sg/context/sis_research/article/7749/viewcontent/10.1007_978_3_642_27355_1.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-77492023-08-21T01:12:29Z Topic based query suggestions for video search WAN, Kong-Wah TAN, Ah-hwee LIM, Joo-Hwee CHIA, Liang-Tien Query suggestion is an assistive technology mechanism commonly used in search engines to enable a user to formulate their search queries by predicting or completing the next few query words that the user is likely to type. In most implementations, the suggestions are mined from query log and use some simple measure of query similarity such as query frequency or lexicographical matching. In this paper, we propose an alternative method of presenting query suggestions by their thematic topics. Our method adopts a document-centric approach to mine topics in the corpus, and does not require the availability of a query log. The heart of our algorithm is a probabilistic topic model that assumes that topics are multinomial distributions of words, and jointly learns the co-occurrence of textual words and the visual information in the video stream. Empirical results show that this alternate way of organizing query suggestions can better elucidate the high level query intent, and more effectively help a user meet his information need. 2012-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6746 info:doi/10.1007/978-3-642-27355-1_28 https://ink.library.smu.edu.sg/context/sis_research/article/7749/viewcontent/10.1007_978_3_642_27355_1.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Topic Modeling Latent Dirichlet Allocation Query Suggestion Databases and Information Systems Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Topic Modeling
Latent Dirichlet Allocation
Query Suggestion
Databases and Information Systems
Graphics and Human Computer Interfaces
spellingShingle Topic Modeling
Latent Dirichlet Allocation
Query Suggestion
Databases and Information Systems
Graphics and Human Computer Interfaces
WAN, Kong-Wah
TAN, Ah-hwee
LIM, Joo-Hwee
CHIA, Liang-Tien
Topic based query suggestions for video search
description Query suggestion is an assistive technology mechanism commonly used in search engines to enable a user to formulate their search queries by predicting or completing the next few query words that the user is likely to type. In most implementations, the suggestions are mined from query log and use some simple measure of query similarity such as query frequency or lexicographical matching. In this paper, we propose an alternative method of presenting query suggestions by their thematic topics. Our method adopts a document-centric approach to mine topics in the corpus, and does not require the availability of a query log. The heart of our algorithm is a probabilistic topic model that assumes that topics are multinomial distributions of words, and jointly learns the co-occurrence of textual words and the visual information in the video stream. Empirical results show that this alternate way of organizing query suggestions can better elucidate the high level query intent, and more effectively help a user meet his information need.
format text
author WAN, Kong-Wah
TAN, Ah-hwee
LIM, Joo-Hwee
CHIA, Liang-Tien
author_facet WAN, Kong-Wah
TAN, Ah-hwee
LIM, Joo-Hwee
CHIA, Liang-Tien
author_sort WAN, Kong-Wah
title Topic based query suggestions for video search
title_short Topic based query suggestions for video search
title_full Topic based query suggestions for video search
title_fullStr Topic based query suggestions for video search
title_full_unstemmed Topic based query suggestions for video search
title_sort topic based query suggestions for video search
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/6746
https://ink.library.smu.edu.sg/context/sis_research/article/7749/viewcontent/10.1007_978_3_642_27355_1.pdf
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