Abstract
To increase accuracy, ensemble approaches have become increasingly popular in designing recommendation systems. More recently, efforts have focused on dynamically integrating base models to improve ensemble performance further. Dynamic integration entails combining multiple base models with dynamically changing contributions to the ensemble. In this paper, we propose a Dynamic-Weighted Stacking (DWS) recommendation model. Bayesian optimization is used to adjust hyperparameters and find the best weight for each base model dynamically. The base models used in the DWS recommendation model are K-Nearest Neighbors (KNN), Linear Regression (LR), and Support Vector Machine (SVM). The proposed ensemble model is tested across various scenarios corresponding to different user populations with varying ratings and user counts, and the results are reported. We also compare the DWS recommendation model to individual static base models. It is observed that the batch dynamic integration approach works better than individual static models and static traditional stacking. The proposed model’s flexibility in handling various data distributions highlights its potential for real-world applications in recommendation systems.
Keywords: Bayesian optimization, Dynamic Ensemble learning, Dynamic Weighted Stacking, K-Nearest Neighbors, Linear Regression, Stacking.