# QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou

arXiv: https://arxiv.org/abs/2411.11739 · HTML: https://mlsanity.com/paper/2411.11739/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 36 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Built on by (most cited first, 28 of 28)

- [MiniOneRec: An Open-Source Framework for Scaling Generative Recommendation](https://mlsanity.com/paper/2510.24431/) (2025)
- [Large Language Model Enhanced Recommender Systems: A Survey](https://mlsanity.com/paper/2412.13432/) (2024)
- [MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation](https://mlsanity.com/paper/2508.15281/) (2025)
- [Search-E1: Self-Distillation Drives Self-Evolution in Search-Augmented Reasoning](https://mlsanity.com/paper/2605.22511/) (2026)
- [Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey](https://mlsanity.com/paper/2507.22920/) (2025)
- [AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems](https://mlsanity.com/paper/2606.26859/) (2026)
- [WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report](https://mlsanity.com/paper/2608.24053/) (2026)
- [Masked Diffusion for Generative Recommendation](https://mlsanity.com/paper/2511.23021/) (2025)
- [Generative Recommendation for Large-Scale Advertising](https://mlsanity.com/paper/2602.22732/) (2026)
- [Understanding Generative Recommendation with Semantic IDs from a Model-scaling View](https://mlsanity.com/paper/2509.25522/) (2025)
- [Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation](https://mlsanity.com/paper/2512.01372/) (2025)
- [Generative Reasoning Recommendation via LLMs](https://mlsanity.com/paper/2510.20815/) (2025)
- [Large Language Model as Universal Retriever in Industrial-Scale Recommender System](https://mlsanity.com/paper/2502.03041/) (2025)
- [Scaling Laws for Online Advertisement Retrieval](https://mlsanity.com/paper/2411.13322/) (2024)
- [LLM-Aligned Geographic Item Tokenization for Local-Life Recommendation](https://mlsanity.com/paper/2511.14221/) (2025)
- [Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders](https://mlsanity.com/paper/2605.16825/) (2026)
- [GRADE: Personalized Multi-Task Fusion via Group-relative Reinforcement Learning with Adaptive Dirichlet Exploration](https://mlsanity.com/paper/2510.07919/) (2025)
- [RQ-Reg: A Residual-Quantization-Based Framework for Continuous Value Prediction in Recommender Systems](https://mlsanity.com/paper/2602.23012/) (2026)
- [PRQ-KMeans: Projection Residual Quantization for Semantic ID Tokenization](https://mlsanity.com/paper/2608.24207/) (2026)
- [DMGIN: How Multimodal LLMs Enhance Large Recommendation Models for Lifelong User Post-click Behaviors](https://mlsanity.com/paper/2508.21801/) (2025)
- [Multimodal Generative Retrieval Model with Staged Pretraining for Food Delivery on Meituan](https://mlsanity.com/paper/2602.06654/) (2026)
- [UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence](https://mlsanity.com/paper/2605.09040/) (2026)
- [WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models](https://mlsanity.com/paper/2607.26621/) (2026)
- [Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation](https://mlsanity.com/paper/2607.27944/) (2026)
- [SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation](https://mlsanity.com/paper/2608.18779/) (2026)
- [SA-RSQ: A Versatile Sparse Representation Framework for Multi-modal Recommender Systems](https://mlsanity.com/paper/2608.22979/) (2026)
- [AlignPoison: Conditional Targeted Poisoning of Generative Recommenders via Decoding-Aligned Prefixes](https://mlsanity.com/paper/2610.14487/) (2026)
- [Beyond ICD Codes: Semantic IDs for Generative Medical Coding](https://mlsanity.com/paper/2610.19301/) (2026)

## Abstract (arXiv)

In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling. In industry, a wide-used modeling architecture is a cascading paradigm: (1) first pre-training a multi-modal model to provide omnipotent representations for downstream services; (2) The downstream recommendation model takes the multi-modal representation as additional input to fit real user-item behaviours. Although such paradigm achieves remarkable improvements, however, there still exist two problems that limit model performance: (1) Representation Unmatching: The pre-trained multi-modal model is always supervised by the classic NLP/CV tasks, while the recommendation models are supervised by real user-item interaction. As a result, the two fundamentally different tasks' goals were relatively separate, and there was a lack of consistent objective on their representations; (2) Representation Unlearning: The generated multi-modal representations are always stored in cache store and serve as extra fixed input of recommendation model, thus could not be updated by recommendation model gradient, further unfriendly for downstream training. Inspired by the two difficulties challenges in downstream tasks usage, we introduce a quantitative multi-modal framework to customize the specialized and trainable multi-modal information for different downstream models.