# LLM-I2I: Boost Your Small Item2Item Recommendation Model with Large Language Model

arXiv: https://arxiv.org/abs/2512.21595 · HTML: https://mlsanity.com/paper/2512.21595/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 25 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [SDM: Sequential Deep Matching Model for Online Large-scale Recommender System](https://mlsanity.com/paper/1909.00385/) (2019)
- [Large Scale Product Graph Construction for Recommendation in E-commerce](https://mlsanity.com/paper/2010.05525/) (2020)
- [Large Language Models Enhanced Collaborative Filtering](https://mlsanity.com/paper/2403.17688/) (2024)
- [Large Language Model Distilling Medication Recommendation Model](https://mlsanity.com/paper/2402.02803/) (2024)
- [Deep Item-based Collaborative Filtering for Top-N Recommendation](https://mlsanity.com/paper/1811.04392/) (2018)
- [Large Language Models as Data Augmenters for Cold-Start Item Recommendation](https://mlsanity.com/paper/2402.11724/) (2024)
- [Equivariant Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2211.05290/) (2022)
- [FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction](https://mlsanity.com/paper/2310.19453/) (2023)

## Abstract (arXiv)

Item-to-Item (I2I) recommendation models are widely used in real-world systems due to their scalability, real-time capabilities, and high recommendation quality. Research to enhance I2I performance focuses on two directions: 1) model-centric approaches, which adopt deeper architectures but risk increased computational costs and deployment complexity, and 2) data-centric methods, which refine training data without altering models, offering cost-effectiveness but struggling with data sparsity and noise. To address these challenges, we propose LLM-I2I, a data-centric framework leveraging Large Language Models (LLMs) to mitigate data quality issues. LLM-I2I includes (1) an LLM-based generator that synthesizes user-item interactions for long-tail items, alleviating data sparsity, and (2) an LLM-based discriminator that filters noisy interactions from real and synthetic data. The refined data is then fused to train I2I models. Evaluated on industry (AEDS) and academic (ARD) datasets, LLM-I2I consistently improves recommendation accuracy, particularly for long-tail items. Deployed on a large-scale cross-border e-commerce platform, it boosts recall number (RN) by 6.02% and gross merchandise value (GMV) by 1.22% over existing I2I models. This work highlights the potential of LLMs in enhancing data-centric recommendation systems without modifying model architectures.