# Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval

arXiv: https://arxiv.org/abs/2504.01403 · HTML: https://mlsanity.com/paper/2504.01403/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023)
- [Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation](https://mlsanity.com/paper/2311.09049/) (2023)
- [Autoregressive Search Engines: Generating Substrings as Document Identifiers](https://mlsanity.com/paper/2204.10628/) (2022)
- [Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)](https://mlsanity.com/paper/1405.5869/) (2014)
- [Scalable and Effective Generative Information Retrieval](https://mlsanity.com/paper/2311.09134/) (2023)
- [Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer](https://mlsanity.com/paper/2208.09257/) (2022)
- [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)
- [Auto Search Indexer for End-to-End Document Retrieval](https://mlsanity.com/paper/2310.12455/) (2023)
- [A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval](https://mlsanity.com/paper/2303.15870/) (2023)
- [A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce](https://mlsanity.com/paper/2408.01928/) (2024)

## Built on by (most cited first, 13 of 13)

- [PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations](https://mlsanity.com/paper/2510.07784/) (2025)
- [ADORE: Autonomous Domain-Oriented Relevance Engine for E-commerce](https://mlsanity.com/paper/2512.02555/) (2025)
- [Aligning Large Language Models with Searcher Preferences](https://mlsanity.com/paper/2603.10473/) (2026)
- [Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising](https://mlsanity.com/paper/2506.03827/) (2025)
- [From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines](https://mlsanity.com/paper/2604.13468/) (2026)
- [Learning Variable-Length Tokenization for Generative Recommendation](https://mlsanity.com/paper/2605.17779/) (2026)
- [Fine-Tuned LLM as a Complementary Predictor Improving Ads System](https://mlsanity.com/paper/2605.27856/) (2026)
- [Multimedia-Aware Question Answering: A Review of Retrieval and Cross-Modal Reasoning Architectures](https://mlsanity.com/paper/2510.20193/) (2025)
- [PRISM: Purified Representation and Integrated Semantic Modeling for Generative Sequential Recommendation](https://mlsanity.com/paper/2601.16556/) (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)
- [Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster](https://mlsanity.com/paper/2608.30606/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

Traditional sparse and dense retrieval methods struggle to leverage general world knowledge and often fail to capture the nuanced features of queries and products. With the advent of large language models (LLMs), industrial search systems have started to employ LLMs to generate identifiers for product retrieval. Commonly used identifiers include (1) static/semantic IDs and (2) product term sets. The first approach requires creating a product ID system from scratch, missing out on the world knowledge embedded within LLMs. While the second approach leverages this general knowledge, the significant difference in word distribution between queries and products means that product-based identifiers often do not align well with user search queries, leading to missed product recalls. Furthermore, when queries contain numerous attributes, these algorithms generate a large number of identifiers, making it difficult to assess their quality, which results in low overall recall efficiency. To address these challenges, this paper introduces a novel e-commerce retrieval paradigm: the Generative Retrieval and Alignment Model (GRAM). GRAM employs joint training on text information from both queries and products to generate shared text identifier codes, effectively bridging the gap between queries and products. This approach not only enhances the connection between queries and products but also improves inference efficiency. The model uses a co-alignment strategy to generate codes optimized for maximizing retrieval efficiency. Additionally, it introduces a query-product scoring mechanism to compare product values across different codes, further boosting retrieval efficiency. Extensive offline and online A/B testing demonstrates that GRAM significantly outperforms traditional models and the latest generative retrieval models, confirming its effectiveness and practicality.