# Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation

arXiv: https://arxiv.org/abs/2308.03470 · HTML: https://mlsanity.com/paper/2308.03470/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2023.

## Built on by (most cited first, 5 of 5)

- [LiGNN: Graph Neural Networks at LinkedIn](https://mlsanity.com/paper/2402.11139/) (2024)
- [Prompt Tuning for Item Cold-start Recommendation](https://mlsanity.com/paper/2412.18082/) (2024)
- [Token-Efficient Item Representation via Images for LLM Recommender Systems](https://mlsanity.com/paper/2503.06238/) (2025)
- [Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search](https://mlsanity.com/paper/2605.17994/) (2026)
- [Anatomy of a Decision: Uncertainty-aware Hierarchical Intent Learning via Flow Matching for Multimodal Recommendation](https://mlsanity.com/paper/2609.29609/) (2026)

## Abstract (arXiv)

Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem, i.e., recommend for few-interaction items. Existing GNN-based recommendation models to address the cold-start problem mainly focus on utilizing auxiliary features of users and items, leaving the user-item interactions under-utilized. However, embeddings distributions of cold and warm items are still largely different, since cold items' embeddings are learned from lower-popularity interactions, while warm items' embeddings are from higher-popularity interactions. Thus, there is a seesaw phenomenon, where the recommendation performance for the cold and warm items cannot be improved simultaneously. To this end, we proposed a Uncertainty-aware Consistency learning framework for Cold-start item recommendation (shorten as UCC) solely based on user-item interactions. Under this framework, we train the teacher model (generator) and student model (recommender) with consistency learning, to ensure the cold items with additionally generated low-uncertainty interactions can have similar distribution with the warm items. Therefore, the proposed framework improves the recommendation of cold and warm items at the same time, without hurting any one of them. Extensive experiments on benchmark datasets demonstrate that our proposed method significantly outperforms state-of-the-art methods on both warm and cold items, with an average performance improvement of 27.6%.