# Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion

arXiv: https://arxiv.org/abs/2502.03664 · HTML: https://mlsanity.com/paper/2502.03664/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms](https://mlsanity.com/paper/2311.07229/) (2023)

## Built on by (most cited first, 11 of 11)

- [Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimization](https://mlsanity.com/paper/2503.23659/) (2025)
- [Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks](https://mlsanity.com/paper/2503.19380/) (2025)
- [Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration](https://mlsanity.com/paper/2504.00282/) (2025)
- [Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference](https://mlsanity.com/paper/2504.05758/) (2025)
- [Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention](https://mlsanity.com/paper/2504.08183/) (2025)
- [Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework](https://mlsanity.com/paper/2503.11125/) (2025)
- [A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Tasks](https://mlsanity.com/paper/2502.16484/) (2025)
- [Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM](https://mlsanity.com/paper/2503.11084/) (2025)
- [A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection](https://mlsanity.com/paper/2503.21160/) (2025)
- [Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction](https://mlsanity.com/paper/2504.04385/) (2025)
- [Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2505.05989/) (2025)

## Abstract (arXiv)

This paper proposes a cold start recommendation model that integrates contrastive learning, aiming to solve the problem of performance degradation of recommendation systems in cold start scenarios due to the scarcity of user and item interaction data. The model dynamically adjusts the weights of key features through an adaptive feature selection module and effectively integrates user attributes, item meta-information, and contextual features by combining a multimodal feature fusion mechanism, thereby improving recommendation performance. In addition, the model introduces a contrastive learning mechanism to enhance the robustness and generalization ability of feature representation by constructing positive and negative sample pairs. Experiments are conducted on the MovieLens-1M dataset. The results show that the proposed model significantly outperforms mainstream recommendation methods such as Matrix Factorization, LightGBM, DeepFM, and AutoRec in terms of HR, NDCG, MRR, and Recall, especially in cold start scenarios. Ablation experiments further verify the key role of each module in improving model performance, and the learning rate sensitivity analysis shows that a moderate learning rate is crucial to the optimization effect of the model. This study not only provides a new solution to the cold start problem but also provides an important reference for the application of contrastive learning in recommendation systems. In the future, this model is expected to play a role in a wider range of scenarios, such as real-time recommendation and cross-domain recommendation.