IEICE TRANSACTIONS on Information
Continuous Similarity Search for Dynamic Text Streams
Summary :
Similarity search for data streams has attracted much attention for information recommendation. In this context, recent leading works regard the latest W items in a data stream as an evolving set and reduce similarity search for data streams to set similarity search. Whereas they consider standard sets composed of items, this paper uniquely studies similarity search for text streams and treats evolving sets whose elements are texts. Specifically, we formulate a new continuous range search problem named the CTS problem (Continuous similarity search for Text Sets). The task of the CTS problem is to find all the text streams from the database whose similarity to the query becomes larger than a threshold ε. It abstracts a scenario in which a user-based recommendation system searches similar users from social networking services. The CTS is important because it allows both the query and the database to change dynamically. We develop a fast pruning-based algorithm for the CTS. Moreover, we discuss how to speed up it with the inverted index.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.12 pp.2026-2035
- Publication Date
- 2023/12/01
- Publicized
- 2023/09/21
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7229
- Type of Manuscript
- PAPER
- Category
- Data Engineering, Web Information Systems
Authors
Yuma TSUCHIDA
University of Electro-Communications
Kohei KUBO
University of Electro-Communications
Hisashi KOGA
University of Electro-Communications
Keyword
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Yuma TSUCHIDA, Kohei KUBO, Hisashi KOGA, "Continuous Similarity Search for Dynamic Text Streams" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 12, pp. 2026-2035, December 2023, doi: 10.1587/transinf.2022EDP7229.
Abstract: Similarity search for data streams has attracted much attention for information recommendation. In this context, recent leading works regard the latest W items in a data stream as an evolving set and reduce similarity search for data streams to set similarity search. Whereas they consider standard sets composed of items, this paper uniquely studies similarity search for text streams and treats evolving sets whose elements are texts. Specifically, we formulate a new continuous range search problem named the CTS problem (Continuous similarity search for Text Sets). The task of the CTS problem is to find all the text streams from the database whose similarity to the query becomes larger than a threshold ε. It abstracts a scenario in which a user-based recommendation system searches similar users from social networking services. The CTS is important because it allows both the query and the database to change dynamically. We develop a fast pruning-based algorithm for the CTS. Moreover, we discuss how to speed up it with the inverted index.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7229/_p
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@ARTICLE{e106-d_12_2026,
author={Yuma TSUCHIDA, Kohei KUBO, Hisashi KOGA, },
journal={IEICE TRANSACTIONS on Information},
title={Continuous Similarity Search for Dynamic Text Streams},
year={2023},
volume={E106-D},
number={12},
pages={2026-2035},
abstract={Similarity search for data streams has attracted much attention for information recommendation. In this context, recent leading works regard the latest W items in a data stream as an evolving set and reduce similarity search for data streams to set similarity search. Whereas they consider standard sets composed of items, this paper uniquely studies similarity search for text streams and treats evolving sets whose elements are texts. Specifically, we formulate a new continuous range search problem named the CTS problem (Continuous similarity search for Text Sets). The task of the CTS problem is to find all the text streams from the database whose similarity to the query becomes larger than a threshold ε. It abstracts a scenario in which a user-based recommendation system searches similar users from social networking services. The CTS is important because it allows both the query and the database to change dynamically. We develop a fast pruning-based algorithm for the CTS. Moreover, we discuss how to speed up it with the inverted index.},
keywords={},
doi={10.1587/transinf.2022EDP7229},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Continuous Similarity Search for Dynamic Text Streams
T2 - IEICE TRANSACTIONS on Information
SP - 2026
EP - 2035
AU - Yuma TSUCHIDA
AU - Kohei KUBO
AU - Hisashi KOGA
PY - 2023
DO - 10.1587/transinf.2022EDP7229
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2023
AB - Similarity search for data streams has attracted much attention for information recommendation. In this context, recent leading works regard the latest W items in a data stream as an evolving set and reduce similarity search for data streams to set similarity search. Whereas they consider standard sets composed of items, this paper uniquely studies similarity search for text streams and treats evolving sets whose elements are texts. Specifically, we formulate a new continuous range search problem named the CTS problem (Continuous similarity search for Text Sets). The task of the CTS problem is to find all the text streams from the database whose similarity to the query becomes larger than a threshold ε. It abstracts a scenario in which a user-based recommendation system searches similar users from social networking services. The CTS is important because it allows both the query and the database to change dynamically. We develop a fast pruning-based algorithm for the CTS. Moreover, we discuss how to speed up it with the inverted index.
ER -
English
