aryan-jadon/Evaluation-Metrics-for-Recommendation-Systems
This repository contains the implementation of evaluation metrics for recommendation systems. We have compared similarity, candidate generation, rating, ranking metrics performance on 5 different datasets - MovieLens 100k, MovieLens 1m, MovieLens 10m, Amazon Electronics Dataset and Amazon Movies and TV Dataset.
This project helps data scientists and machine learning engineers understand how well different recommendation systems perform. It takes various recommendation models and evaluates them using a range of metrics, showing you which algorithms are best suited for different business goals. You'll get comprehensive performance reports on metrics like similarity, candidate generation, rating, and ranking.
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Use this if you are building or optimizing a recommendation engine and need to rigorously compare different models to select the most effective one.
Not ideal if you are a business user looking for a ready-to-use recommendation system without needing to dive into model evaluation metrics.
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Feb 21, 2025
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