# POSO: Personalized Cold Start Modules for Large-scale Recommender Systems

arXiv: https://arxiv.org/abs/2108.04690 · HTML: https://mlsanity.com/paper/2108.04690/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2021.

## Built on by (most cited first, 9 of 9)

- [Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review](https://mlsanity.com/paper/2402.18590/) (2024)
- [Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systems](https://mlsanity.com/paper/2405.13238/) (2024)
- [ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users](https://mlsanity.com/paper/2510.09393/) (2025)
- [DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction](https://mlsanity.com/paper/2301.08139/) (2023)
- [HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2408.01332/) (2024)
- [Adaptive Conditional Expert Selection Network for Multi-domain Recommendation](https://mlsanity.com/paper/2411.06826/) (2024)
- [Causality Enhancement for Cross-Domain Recommendation](https://mlsanity.com/paper/2510.14641/) (2025)
- [DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation](https://mlsanity.com/paper/2602.13971/) (2026)
- [Dual-Attention Convolution Experts for Sparse Tensor Completion](https://mlsanity.com/paper/2606.21427/) (2026)

## Abstract (arXiv)

Recommendation for new users, also called user cold start, has been a well-recognized challenge for online recommender systems. Most existing methods view the crux as the lack of initial data. However, in this paper, we argue that there are neglected problems: 1) New users' behaviour follows much different distributions from regular users. 2) Although personalized features are involved, heavily imbalanced samples prevent the model from balancing new/regular user distributions, as if the personalized features are overwhelmed. We name the problem as the "submergence" of personalization. To tackle this problem, we propose a novel module: Personalized COld Start MOdules (POSO). Considering from a model architecture perspective, POSO personalizes existing modules by introducing multiple user-group-specialized sub-modules. Then, it fuses their outputs by personalized gates, resulting in comprehensive representations. In such way, POSO projects imbalanced features to even modules. POSO can be flexibly integrated into many existing modules and effectively improves their performance with negligible computational overheads. The proposed method shows remarkable advantage in industrial scenario. It has been deployed on the large-scale recommender system of Kwai, and improves new user Watch Time by a large margin (+7.75%). Moreover, POSO can be further generalized to regular users, inactive users and returning users (+2%-3% on Watch Time), as well as item cold start (+3.8% on Watch Time). Its effectiveness has also been verified on public dataset (MovieLens 20M). We believe such practical experience can be well generalized to other scenarios.