Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion
This addresses the performance degradation in recommendation systems for new users or items with scarce interaction data, though it appears incremental as it builds on existing contrastive learning and feature fusion techniques.
The paper tackles the cold start problem in recommendation systems by proposing a model that integrates contrastive learning and adaptive feature fusion, achieving significant performance improvements over methods like Matrix Factorization and DeepFM on the MovieLens-1M dataset in terms of metrics such as HR, NDCG, MRR, and Recall.
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.