Collaborative Filtering Algorithm

MATLAB 1.24M 189 views 0 downloads 1 credits
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Resource Overview

This implementation covers both user-based and item-based collaborative filtering approaches (detailed in README) using the provided dataset for training and evaluation.

Detailed Documentation

This document discusses collaborative filtering algorithms, specifically focusing on user-based and item-based approaches as detailed in our README file. The implementation utilizes a dataset that serves as the primary data source for training the recommendation system. The dataset plays a crucial role in the algorithm's effectiveness, providing the necessary user-item interaction data for calculating similarity matrices and generating predictions. Through proper dataset preprocessing and feature engineering, the algorithm learns patterns from user behavior to make accurate recommendations. Key implementation aspects include computing similarity scores using cosine similarity or Pearson correlation, generating neighborhood sets for target users/items, and aggregating predictions based on weighted averages of similar entities.

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