# Contrastive Learning for Cold-Start Recommendation

arXiv: https://arxiv.org/abs/2107.05315 · HTML: https://mlsanity.com/paper/2107.05315/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2021.

## Built on by (most cited first, 30 of 79)

- [RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs](https://mlsanity.com/paper/2503.10657/) (2025)
- [Personalized Showcases: Generating Multi-Modal Explanations for Recommendations](https://mlsanity.com/paper/2207.00422/) (2022)
- [Heterogeneous Contrastive Learning for Foundation Models and Beyond](https://mlsanity.com/paper/2404.00225/) (2024)
- [DiffMM: Multi-Modal Diffusion Model for Recommendation](https://mlsanity.com/paper/2406.11781/) (2024)
- [LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation](https://mlsanity.com/paper/2506.21579/) (2025)
- [Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation](https://mlsanity.com/paper/2111.00678/) (2021)
- [Enhancing Sequential Recommendation with Graph Contrastive Learning](https://mlsanity.com/paper/2205.14837/) (2022)
- [Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis](https://mlsanity.com/paper/2207.11652/) (2022)
- [Image Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2502.15694/) (2024)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://mlsanity.com/paper/2306.14462/) (2023)
- [Multimodality Invariant Learning for Multimedia-Based New Item Recommendation](https://mlsanity.com/paper/2405.15783/) (2024)
- [A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios](https://mlsanity.com/paper/2409.17864/) (2024)
- [Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization](https://mlsanity.com/paper/2403.03419/) (2024)
- [LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2506.17966/) (2025)
- [EasyRec: Simple yet Effective Language Models for Recommendation](https://mlsanity.com/paper/2408.08821/) (2024)
- [ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems](https://mlsanity.com/paper/2409.11690/) (2024)
- [GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation](https://mlsanity.com/paper/2303.00243/) (2023)
- [Multi-Sample based Contrastive Loss for Top-k Recommendation](https://mlsanity.com/paper/2109.00217/) (2021)
- [Click-through Rate Prediction with Auto-Quantized Contrastive Learning](https://mlsanity.com/paper/2109.13921/) (2021)
- [RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification](https://mlsanity.com/paper/2308.02335/) (2023)
- [Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation](https://mlsanity.com/paper/2504.02458/) (2025)
- [FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2204.09850/) (2022)
- [Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation](https://mlsanity.com/paper/2306.14179/) (2023)
- [How Does Message Passing Improve Collaborative Filtering?](https://mlsanity.com/paper/2404.08660/) (2024)
- [Modality-Balanced Learning for Multimedia Recommendation](https://mlsanity.com/paper/2408.06360/) (2024)
- [Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating](https://mlsanity.com/paper/2310.03813/) (2023)
- [Graph Augmentation for Recommendation](https://mlsanity.com/paper/2403.16656/) (2024)
- [SocialRec: User Activity Based Post Weighted Dynamic Personalized Post Recommendation System in Social Media](https://mlsanity.com/paper/2407.09747/) (2024)
- [Multi-Behavior Recommender Systems: A Survey](https://mlsanity.com/paper/2503.06963/) (2025)

## Abstract (arXiv)

Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information into the CF scheme. Specifically, they employ modern neural network techniques (e.g., dropout, consistency constraint) to discover and exploit the coalition effect of content features and collaborative representations. However, we argue that these works less explore the mutual dependencies between content features and collaborative representations and lack sufficient theoretical supports, thus resulting in unsatisfactory performance. In this work, we reformulate the cold-start item representation learning from an information-theoretic standpoint. It aims to maximize the mutual dependencies between item content and collaborative signals. Specifically, the representation learning is theoretically lower-bounded by the integration of two terms: mutual information between collaborative embeddings of users and items, and mutual information between collaborative embeddings and feature representations of items. To model such a learning process, we devise a new objective function founded upon contrastive learning and develop a simple yet effective Contrastive Learning-based Cold-start Recommendation framework(CLCRec). In particular, CLCRec consists of three components: contrastive pair organization, contrastive embedding, and contrastive optimization modules. It allows us to preserve collaborative signals in the content representations for both warm and cold-start items. Through extensive experiments on four publicly accessible datasets, we observe that CLCRec achieves significant improvements over state-of-the-art approaches in both warm- and cold-start scenarios.