@inproceedings{saadany-etal-2024-centrality,
title = "Centrality-aware Product Retrieval and Ranking",
author = "Saadany, Hadeel and
Bhosale, Swapnil and
Agrawal, Samarth and
Kanojia, Diptesh and
Orasan, Constantin and
Wu, Zhe",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.17",
pages = "215--224",
abstract = "This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to user{'}s search queries. Ambiguity and complexity of user queries often lead to a mismatch between user{'}s intent and retrieved product titles or documents. Recent approaches have proposed the use of Transformer-based models which need millions of annotated query-title pairs during the pre-training stage, and this data often does not take user intent into account. To tackle this, we curate samples from existing datasets at eBay, manually annotated with buyer-centric relevance scores, and centrality scores which reflect how well the product title matches the user{'}s intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimizes for the user intent in semantic product search. To that end, we propose a dual-loss based optimization to handle hard negatives, i.e., product titles that are semantically relevant but do not reflect the user{'}s intent. Our contributions include curating challenging evaluation sets and implementing UCO, resulting in significant improvements in product ranking efficiency, observed for different evaluation metrics. Our work aims to ensure that the most buyer-centric titles for a query are ranked higher, thereby, enhancing the user experience on e-commerce platforms.",
}
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<abstract>This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to user’s search queries. Ambiguity and complexity of user queries often lead to a mismatch between user’s intent and retrieved product titles or documents. Recent approaches have proposed the use of Transformer-based models which need millions of annotated query-title pairs during the pre-training stage, and this data often does not take user intent into account. To tackle this, we curate samples from existing datasets at eBay, manually annotated with buyer-centric relevance scores, and centrality scores which reflect how well the product title matches the user’s intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimizes for the user intent in semantic product search. To that end, we propose a dual-loss based optimization to handle hard negatives, i.e., product titles that are semantically relevant but do not reflect the user’s intent. Our contributions include curating challenging evaluation sets and implementing UCO, resulting in significant improvements in product ranking efficiency, observed for different evaluation metrics. Our work aims to ensure that the most buyer-centric titles for a query are ranked higher, thereby, enhancing the user experience on e-commerce platforms.</abstract>
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%0 Conference Proceedings
%T Centrality-aware Product Retrieval and Ranking
%A Saadany, Hadeel
%A Bhosale, Swapnil
%A Agrawal, Samarth
%A Kanojia, Diptesh
%A Orasan, Constantin
%A Wu, Zhe
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F saadany-etal-2024-centrality
%X This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to user’s search queries. Ambiguity and complexity of user queries often lead to a mismatch between user’s intent and retrieved product titles or documents. Recent approaches have proposed the use of Transformer-based models which need millions of annotated query-title pairs during the pre-training stage, and this data often does not take user intent into account. To tackle this, we curate samples from existing datasets at eBay, manually annotated with buyer-centric relevance scores, and centrality scores which reflect how well the product title matches the user’s intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimizes for the user intent in semantic product search. To that end, we propose a dual-loss based optimization to handle hard negatives, i.e., product titles that are semantically relevant but do not reflect the user’s intent. Our contributions include curating challenging evaluation sets and implementing UCO, resulting in significant improvements in product ranking efficiency, observed for different evaluation metrics. Our work aims to ensure that the most buyer-centric titles for a query are ranked higher, thereby, enhancing the user experience on e-commerce platforms.
%U https://aclanthology.org/2024.emnlp-industry.17
%P 215-224
Markdown (Informal)
[Centrality-aware Product Retrieval and Ranking](https://aclanthology.org/2024.emnlp-industry.17) (Saadany et al., EMNLP 2024)
ACL
- Hadeel Saadany, Swapnil Bhosale, Samarth Agrawal, Diptesh Kanojia, Constantin Orasan, and Zhe Wu. 2024. Centrality-aware Product Retrieval and Ranking. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 215–224, Miami, Florida, US. Association for Computational Linguistics.