# Generative Retrieval with Preference Optimization for E-commerce Search

arXiv: https://arxiv.org/abs/2407.19829 · HTML: https://mlsanity.com/paper/2407.19829/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Preference alignment for recommenders and RAG, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Preference alignment for recommenders and RAG](https://mlsanity.com/alignment/preference-alignment-for-recommenders-and-rag/)

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [Autoregressive Search Engines: Generating Substrings as Document Identifiers](https://mlsanity.com/paper/2204.10628/) (2022)
- [A Neural Corpus Indexer for Document Retrieval](https://mlsanity.com/paper/2206.02743/) (2022)
- [Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation](https://mlsanity.com/paper/2206.10128/) (2022)
- [Learning to Rank in Generative Retrieval](https://mlsanity.com/paper/2306.15222/) (2023)
- [Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning](https://mlsanity.com/paper/2006.02282/) (2020)
- [Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies](https://mlsanity.com/paper/2305.15115/) (2023)
- [GLEN: Generative Retrieval via Lexical Index Learning](https://mlsanity.com/paper/2311.03057/) (2023)
- [Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index](https://mlsanity.com/paper/2105.03933/) (2021)
- [Generative Dense Retrieval: Memory Can Be a Burden](https://mlsanity.com/paper/2401.10487/) (2024)
- [Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search](https://mlsanity.com/paper/2208.06150/) (2022)

## Built on by (most cited first, 10 of 10)

- [Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval](https://mlsanity.com/paper/2407.21488/) (2024)
- [SynerGen: Contextualized Generative Recommender for Unified Search and Recommendation](https://mlsanity.com/paper/2509.21777/) (2025)
- [pEBR: A Probabilistic Approach to Embedding Based Retrieval](https://mlsanity.com/paper/2410.19349/) (2024)
- [Test-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations](https://mlsanity.com/paper/2508.18132/) (2025)
- [Synthetic Data Powers Product Retrieval for Long-tail Knowledge-Intensive Queries in E-commerce Search](https://mlsanity.com/paper/2602.23620/) (2026)
- [SIA: A Synthesize-Inject-Align Framework for Knowledge-Grounded and Secure E-commerce Search LLMs with Industrial Deployment](https://mlsanity.com/paper/2603.16137/) (2026)
- [UniGD: A Unified Generative-Discriminative Framework for Industrial Retrieval](https://mlsanity.com/paper/2608.03150/) (2026)
- [PailitaoGR: Latent Think-with-Images for Generative Image Retrieval](https://mlsanity.com/paper/2608.26658/) (2026)
- [FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation](https://mlsanity.com/paper/2609.36670/) (2026)
- [WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat](https://mlsanity.com/paper/2610.16979/) (2026)

## Abstract (arXiv)

Generative retrieval introduces a groundbreaking paradigm to document retrieval by directly generating the identifier of a pertinent document in response to a specific query. This paradigm has demonstrated considerable benefits and potential, particularly in representation and generalization capabilities, within the context of large language models. However, it faces significant challenges in E-commerce search scenarios, including the complexity of generating detailed item titles from brief queries, the presence of noise in item titles with weak language order, issues with long-tail queries, and the interpretability of results. To address these challenges, we have developed an innovative framework for E-commerce search, called generative retrieval with preference optimization. This framework is designed to effectively learn and align an autoregressive model with target data, subsequently generating the final item through constraint-based beam search. By employing multi-span identifiers to represent raw item titles and transforming the task of generating titles from queries into the task of generating multi-span identifiers from queries, we aim to simplify the generation process. The framework further aligns with human preferences using click data and employs a constrained search method to identify key spans for retrieving the final item, thereby enhancing result interpretability. Our extensive experiments show that this framework achieves competitive performance on a real-world dataset, and online A/B tests demonstrate the superiority and effectiveness in improving conversion gains.