# Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks

arXiv: https://arxiv.org/abs/2105.04790 · HTML: https://mlsanity.com/paper/2105.04790/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2021.

## Built on by (most cited first, 30 of 37)

- [Personalized Transfer of User Preferences for Cross-domain Recommendation](https://mlsanity.com/paper/2110.11154/) (2021)
- [User-Centric Conversational Recommendation with Multi-Aspect User Modeling](https://mlsanity.com/paper/2204.09263/) (2022)
- [Multi-view Multi-behavior Contrastive Learning in Recommendation](https://mlsanity.com/paper/2203.10576/) (2022)
- [Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation](https://mlsanity.com/paper/2305.05848/) (2023)
- [Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework](https://mlsanity.com/paper/2201.10980/) (2022)
- [Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://mlsanity.com/paper/2306.14462/) (2023)
- [Text Matching Improves Sequential Recommendation by Reducing Popularity Biases](https://mlsanity.com/paper/2308.14029/) (2023)
- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [Sequential LLM Framework for Fashion Recommendation](https://mlsanity.com/paper/2410.11327/) (2024)
- [VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results](https://mlsanity.com/paper/2509.02969/) (2025)
- [Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling](https://mlsanity.com/paper/2208.09130/) (2022)
- [Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model](https://mlsanity.com/paper/2405.05606/) (2024)
- [Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction](https://mlsanity.com/paper/2408.07278/) (2024)
- [Delving Deep into Engagement Prediction of Short Videos](https://mlsanity.com/paper/2410.00289/) (2024)
- [Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation](https://mlsanity.com/paper/2501.07110/) (2025)
- [Addressing Cold-start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling](https://mlsanity.com/paper/2504.06270/) (2025)
- [Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations](https://mlsanity.com/paper/2405.19612/) (2024)
- [Proxy-based Item Representation for Attribute and Context-aware Recommendation](https://mlsanity.com/paper/2312.06145/) (2023)
- [Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systems](https://mlsanity.com/paper/2405.13238/) (2024)
- [Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest](https://mlsanity.com/paper/2512.17277/) (2025)
- [AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation](https://mlsanity.com/paper/2308.06965/) (2023)
- [Prompt Tuning for Item Cold-start Recommendation](https://mlsanity.com/paper/2412.18082/) (2024)
- [AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System](https://mlsanity.com/paper/2501.15816/) (2025)
- [Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization](https://mlsanity.com/paper/2507.19473/) (2025)
- [M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation](https://mlsanity.com/paper/2508.00452/) (2025)
- [Asymmetric Generative Recommendation via Kronecker Residual Bridge and Multi-Faceted Hierarchical Quantization](https://mlsanity.com/paper/2605.14512/) (2026)
- [DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation](https://mlsanity.com/paper/2606.12245/) (2026)
- [Customized Conversational Recommender Systems](https://mlsanity.com/paper/2207.00814/) (2022)
- [RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction](https://mlsanity.com/paper/2210.16080/) (2022)
- [Meta-Shop: Improving Item Advertisement For Small Businesses](https://mlsanity.com/paper/2212.01414/) (2022)

## Abstract (arXiv)

Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem. Especially, for the cold-start item which only has limited interactions, it is hard to train a reasonable item ID embedding, called cold ID embedding, which is a major challenge for the embedding techniques. The cold item ID embedding has two main problems: (1) A gap is existing between the cold ID embedding and the deep model. (2) Cold ID embedding would be seriously affected by noisy interaction. However, most existing methods do not consider both two issues in the cold-start problem, simultaneously. To address these problems, we adopt two key ideas: (1) Speed up the model fitting for the cold item ID embedding (fast adaptation). (2) Alleviate the influence of noise. Along this line, we propose Meta Scaling and Shifting Networks to generate scaling and shifting functions for each item, respectively. The scaling function can directly transform cold item ID embeddings into warm feature space which can fit the model better, and the shifting function is able to produce stable embeddings from the noisy embeddings. With the two meta networks, we propose Meta Warm Up Framework (MWUF) which learns to warm up cold ID embeddings. Moreover, MWUF is a general framework that can be applied upon various existing deep recommendation models. The proposed model is evaluated on three popular benchmarks, including both recommendation and advertising datasets. The evaluation results demonstrate its superior performance and compatibility.