Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
This addresses computational efficiency and accuracy challenges for cold-start recommender systems, though it appears incremental as it builds on existing hybrid matrix factorization and feature selection techniques.
The paper tackles the problem of irrelevant or noisy features degrading performance and increasing computational demands in cold-start recommender systems by proposing a feature selection strategy that prioritizes user behavioral information. The method achieves superior efficiency and surpasses existing techniques in cold-start scenarios while maintaining high accuracy with minimal feature subsets.
Cold-start challenges in recommender systems necessitate leveraging auxiliary features beyond user-item interactions. However, the presence of irrelevant or noisy features can degrade predictive performance, whereas an excessive number of features increases computational demands, leading to higher memory consumption and prolonged training times. To address this, we propose a feature selection strategy that prioritizes the user behavioral information. Our method enhances the feature representation by incorporating correlations from collaborative behavior data using a hybrid matrix factorization technique and then ranks features using a mechanism based on the maximum volume algorithm. This approach identifies the most influential features, striking a balance between recommendation accuracy and computational efficiency. We conduct an extensive evaluation across various datasets and hybrid recommendation models, demonstrating that our method excels in cold-start scenarios by selecting minimal yet highly effective feature subsets. Even under strict feature reduction, our approach surpasses existing feature selection techniques while maintaining superior efficiency.