Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2023
Abstract
In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.
Keywords
Multi-lingual similarity, Multi-partite matching
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the World Wide Web Conference: WWW 2023
First Page
99
Last Page
102
ISBN
9781450394161
Identifier
10.1145/3543873.3587322
Publisher
ACM
City or Country
New York, USA
Citation
TAY, Huan Lin; TAY, Wei Jie; and LAUW, Hady Wirawan.
Multi-lingual multi-partite product title matching. (2023). Proceedings of the World Wide Web Conference: WWW 2023. 99-102.
Available at: https://ink.library.smu.edu.sg/sis_research/8308
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3543873.3587322