Hybrid Collaborative

Hybrid Collaborative Filtering Module

This module implements hybrid collaborative filtering techniques that combine multiple recommendation strategies to improve accuracy and robustness. Hybrid methods leverage the strengths of different algorithms, such as collaborative filtering, content-based filtering, and others, to provide more personalized recommendations.

Key Features: - Combines collaborative and content-based filtering methods. - Utilizes ensemble techniques to enhance recommendation performance. - Supports various hybridization strategies, including weighted, switching, and mixed hybrid approaches.

Classes: - HYBRID_COLLABORATIVE: Main class implementing hybrid collaborative filtering logic.

Usage: To use this module, instantiate the HYBRID_COLLABORATIVE class and call its methods to train and generate recommendations based on your dataset.

Example

hybrid_cf = HYBRID_COLLABORATIVE() hybrid_cf.train(user_item_matrix, content_features) recommendations = hybrid_cf.recommend(user_id, top_n=10)