Hybrid Ensemble

Ensemble Methods for Hybrid Recommendation Systems

This module implements ensemble methods that combine multiple recommendation models to improve overall system performance. Ensemble methods leverage the diversity of different models to enhance prediction accuracy and robustness.

Key Features: - Supports various ensemble strategies, including bagging, boosting, and stacking. - Combines outputs from multiple models to generate final recommendations. - Provides flexibility in model selection and ensemble configuration.

• ENSEMBLE_METHODS: Main class implementing ensemble techniques for hybrid recommendation systems.

Usage: Create an instance of the ENSEMBLE_METHODS class to configure and apply ensemble techniques to your recommendation models. Use the provided methods to train and generate ensemble-based recommendations.

Example

ensemble_model = ENSEMBLE_METHODS() ensemble_model.train(models_list, user_item_matrix) final_recommendations = ensemble_model.recommend(user_id, top_n=10)