Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion

TL;DR

This paper introduces a novel cold start recommendation model that combines contrastive learning with adaptive feature fusion, significantly improving performance in cold start scenarios by enhancing feature robustness and generalization.

Contribution

The paper presents a new recommendation model integrating contrastive learning with adaptive feature selection and multimodal feature fusion, addressing cold start challenges effectively.

Findings

Outperforms mainstream methods like Matrix Factorization and DeepFM in cold start scenarios.

Demonstrates significant improvements in HR, NDCG, MRR, and Recall metrics.

Ablation studies confirm the importance of each module in the model's performance.

Abstract

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.