# Centrality-aware Product Retrieval and Ranking

arXiv: https://arxiv.org/abs/2410.15930 · HTML: https://mlsanity.com/paper/2410.15930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://mlsanity.com/paper/2010.06467/) (2020)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [SGPT: GPT Sentence Embeddings for Semantic Search](https://mlsanity.com/paper/2202.08904/) (2022)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [A Deep Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/1711.08611/) (2017)
- [Learning to Match Using Local and Distributed Representations of Text for Web Search](https://mlsanity.com/paper/1610.08136/) (2016)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations](https://mlsanity.com/paper/1511.08277/) (2015)
- [Pre-trained Language Model for Web-scale Retrieval in Baidu Search](https://mlsanity.com/paper/2106.03373/) (2021)
- [Neural Interactive Collaborative Filtering](https://mlsanity.com/paper/2007.02095/) (2020)
- [Semantic Modelling with Long-Short-Term Memory for Information Retrieval](https://mlsanity.com/paper/1412.6629/) (2014)
- [Memory-efficient Embedding for Recommendations](https://mlsanity.com/paper/2006.14827/) (2020)
- [A unified Neural Network Approach to E-CommerceRelevance Learning](https://mlsanity.com/paper/2104.12302/) (2021)
- [ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction](https://mlsanity.com/paper/2203.06591/) (2022)

## Built on by (most cited first, 3 of 3)

- [NEAR2: A Nested Embedding Approach to Efficient Product Retrieval and Ranking](https://mlsanity.com/paper/2506.19743/) (2025)
- [AI Guided Accelerator For Search Experience](https://mlsanity.com/paper/2508.05649/) (2025)
- [Beyond Relevance: Structured Semantic Supervision for Product Search with LLM-Augmented Annotations](https://mlsanity.com/paper/2609.23646/) (2026)

## Abstract (arXiv)

This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to users' search queries. Ambiguity and complexity of user queries often lead to a mismatch between the 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 users' intent. We introduce a User-intent Centrality Optimization (UCO) approach for existing models, which optimises for the user intent in semantic product search. To that end, we propose a dual-loss based optimisation 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 product ranking efficiency improvements 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.