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.
Summary of experiment with instructions on how to replicate this experiment can be find below.

About Recommendations Models

Majority of this repository work is taken from - https://github.com/recommenders-team/recommenders

Experiments Summary and Our Paper

Cite Our Paper

@inproceedings{jadon2024comprehensive,
  title={A comprehensive survey of evaluation techniques for recommendation systems},
  author={Jadon, Aryan and Patil, Avinash},
  booktitle={International Conference on Computation of Artificial Intelligence \& Machine Learning},
  pages={281--304},
  year={2024},
  organization={Springer}
}

Paper Links

1. https://link.springer.com/chapter/10.1007/978-3-031-71484-9_25
2. https://arxiv.org/abs/2312.16015

Summary of Experiments

Similarity Metrics

Candidate Generation Metrics

Rating Metrics

Ranking Metrics

Replicating this Repository and Experiments

• recommenders: Folder containing the recommendations algorithms implementations.
• similarity_metrics: Folder containing scripts for running experiments of similarity metrics.
• candidate_generation_metrics: Folder containing scripts for running experiments of candidate generations metrics.
• rating_metrics: Folder containing scripts for running experiments of rating metrics.
• ranking_metrics: Folder containing scripts for running experiments of ranking metrics.

Creating Environment

Install the dependencies using requirements.txt