# TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios

arXiv: https://arxiv.org/abs/2508.11977 · HTML: https://mlsanity.com/paper/2508.11977/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 16 Aug 2025.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Better & Faster Large Language Models via Multi-token Prediction](https://mlsanity.com/paper/2404.19737/) (2024)
- [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023)
- [Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations](https://mlsanity.com/paper/2402.17152/) (2024)
- [Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation](https://mlsanity.com/paper/2311.09049/) (2023)
- [OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment](https://mlsanity.com/paper/2502.18965/) (2025)
- [A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions](https://mlsanity.com/paper/2109.12843/) (2021)
- [HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling](https://mlsanity.com/paper/2409.12740/) (2024)
- [E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation](https://mlsanity.com/paper/2312.02443/) (2023)
- [Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations](https://mlsanity.com/paper/2503.02453/) (2025)
- [Large Scale Product Graph Construction for Recommendation in E-commerce](https://mlsanity.com/paper/2010.05525/) (2020)
- [Scaling Law of Large Sequential Recommendation Models](https://mlsanity.com/paper/2311.11351/) (2023)
- [A Comprehensive Survey on Retrieval Methods in Recommender Systems](https://mlsanity.com/paper/2407.21022/) (2024)
- [STAR: A Simple Training-free Approach for Recommendations using Large Language Models](https://mlsanity.com/paper/2410.16458/) (2024)
- [RecFlow: An Industrial Full Flow Recommendation Dataset](https://mlsanity.com/paper/2410.20868/) (2024)
- [EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations](https://mlsanity.com/paper/2405.11441/) (2024)
- [SessionRec: Next Session Prediction Paradigm For Generative Sequential Recommendation](https://mlsanity.com/paper/2502.10157/) (2025)

## Built on by (most cited first, 5 of 5)

- [Beyond Interleaving: Causal Attention Reformulations for Generative Recommender Systems](https://mlsanity.com/paper/2603.10369/) (2026)
- [AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation](https://mlsanity.com/paper/2603.19710/) (2026)
- [When KV Meets Embeddings: Dynamic GPU Memory Allocation for Accelerating Generative Recommender Serving](https://mlsanity.com/paper/2605.04450/) (2026)
- [Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search](https://mlsanity.com/paper/2605.17994/) (2026)
- [Decoupled Temporal Encoding for Generative Recommendation](https://mlsanity.com/paper/2608.16274/) (2026)

## Abstract (arXiv)

Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative models have demonstrated potential in enhancing recommendation systems; however, these models often exhibit limitations in optimizing retrieval tasks, primarily due to their reliance on autoregressive generation mechanisms. Conventional approaches introduce sequential dependencies that impede efficient retrieval, as they are inherently unsuitable for generating multiple items without positional constraints within a single request session. To address these limitations, we propose TBGRecall, a framework integrating Next Session Prediction (NSP), designed to enhance generative retrieval models for e-commerce applications. Our framework reformulation involves partitioning input samples into multi-session sequences, where each sequence comprises a session token followed by a set of item tokens, and then further incorporate multiple optimizations tailored to the generative task in retrieval scenarios. In terms of training methodology, our pipeline integrates limited historical data pre-training with stochastic partial incremental training, significantly improving training efficiency and emphasizing the superiority of data recency over sheer data volume. Our extensive experiments, conducted on public benchmarks alongside a large-scale industrial dataset from TaoBao, show TBGRecall outperforms the state-of-the-art recommendation methods, and exhibits a clear scaling law trend. Ultimately, NSP represents a significant advancement in the effectiveness of generative recommendation systems for e-commerce applications.