# Sequential LLM Framework for Fashion Recommendation

arXiv: https://arxiv.org/abs/2410.11327 · HTML: https://mlsanity.com/paper/2410.11327/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 30 of 33)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [Text Is All You Need: Learning Language Representations for Sequential Recommendation](https://mlsanity.com/paper/2305.13731/) (2023)
- [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [A Simple Convolutional Generative Network for Next Item Recommendation](https://mlsanity.com/paper/1808.05163/) (2018)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion](https://mlsanity.com/paper/1905.01866/) (2019)
- [Visually-Aware Fashion Recommendation and Design with Generative Image Models](https://mlsanity.com/paper/1711.02231/) (2017)
- [Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation](https://mlsanity.com/paper/2307.09688/) (2023)
- [MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation](https://mlsanity.com/paper/2007.03183/) (2020)
- [Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks](https://mlsanity.com/paper/2105.04790/) (2021)
- [CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space](https://mlsanity.com/paper/2204.11067/) (2022)
- [A Review of Modern Fashion Recommender Systems](https://mlsanity.com/paper/2202.02757/) (2022)
- [Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach](https://mlsanity.com/paper/1905.12862/) (2019)
- [When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation](https://mlsanity.com/paper/2305.10822/) (2023)
- [EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM](https://mlsanity.com/paper/2404.08886/) (2024)
- [Improving Outfit Recommendation with Co-supervision of Fashion Generation](https://mlsanity.com/paper/1908.09104/) (2019)

## Built on by (most cited first, 6 of 6)

- [Watermark under Fire: A Robustness Evaluation of LLM Watermarking](https://mlsanity.com/paper/2411.13425/) (2024)
- [Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations](https://mlsanity.com/paper/2508.08454/) (2025)
- [TATTOO: Training-free AesTheTic-aware Outfit recOmmendation](https://mlsanity.com/paper/2509.23242/) (2025)
- [Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce](https://mlsanity.com/paper/2503.13518/) (2025)
- [Fine-Grained Customized Fashion Design with Image-into-Prompt benchmark and dataset from LMM](https://mlsanity.com/paper/2509.09324/) (2025)
- [Unifying Inductive, Cross-Domain, and Multimodal Learning for Robust and Generalizable Recommendation](https://mlsanity.com/paper/2510.21812/) (2025)

## Abstract (arXiv)

The fashion industry is one of the leading domains in the global e-commerce sector, prompting major online retailers to employ recommendation systems for product suggestions and customer convenience. While recommendation systems have been widely studied, most are designed for general e-commerce problems and struggle with the unique challenges of the fashion domain. To address these issues, we propose a sequential fashion recommendation framework that leverages a pre-trained large language model (LLM) enhanced with recommendation-specific prompts. Our framework employs parameter-efficient fine-tuning with extensive fashion data and introduces a novel mix-up-based retrieval technique for translating text into relevant product suggestions. Extensive experiments show our proposed framework significantly enhances fashion recommendation performance.