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Cited by 1Data Quality and ManagementTopic ModelingRecommender Systems and Techniques

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A Large-scale Offer Alignment Model for Partitioning Filtering and Matching Product Offers

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Huang, Wenyu, Melo, André, Pan, Jeff Z.·2024·Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
DOI 10.1145/3626772.3661351
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Abstract

Offer alignment is a key step in a product knowledge graph construction pipeline. It aims to align retailer offers of the same product for better coverage of product details. With the rapid development of online shopping services, the offer alignment task is applied in ever larger datasets. This work aims to build an offer alignment system that can efficiently be used in large-scale offer data. The key components of this system include: 1) common offer encoders for encoding text offer data into representations; 2) trainable LSH partitioning module to divide similar offers into small blocks; 3) lightweight sophisticated late-interactions for efficient filtering and scoring of offer alignment candidate pairs. We evaluate the system on public WDC offer alignment dataset, as well as DBLP-Scholar and DBLP-ACM.

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Crossref · 10.1145/3626772.3661351

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  • These shortcuts may benefit existing product recommendation benchmarks, but are not aligned with the intent understanding objective of retrieving superficially distinct kinds of products serving common intents.

    “These shortcuts may benefit existing product recommendation benchmarks, but are not aligned with the intent understanding objective, namely, to retrieve superficially distinct kinds of products serving common intents”

    From A Usage-centric Take on Intent Understanding in E-Commerce (2024) · cites this paper as Huang et al., 2024

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Research works printed but not linked: 17

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Metadata

Authors
Huang, W., Melo, A., & Pan, J. Z.
Affiliations
  • University of Edinburgh (GB)
  • Huawei Technologies (United Kingdom) (GB)
ORCID
Venue
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher
ACM
Published
2024-07-10
Pages
2880-2884
DOI
10.1145/3626772.3661351
OpenAlex
W4400529386
Cited by
0
References
1
FWCI
0.00
Type
conference paper
Language
en
Open access
Licence
Locations
International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion, cc-by-nc-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Data Quality and Management
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques

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