leorrose / animers

An anime recommendation system

AnimeRs

An anime recommendation system.

The purpose of this project is to research and create an anime recommendation system.

This project was created with Python (version 3.8.7), surprise, pandas, numpy and more libraries.

Project Research

In order to understand the steps and what we did you are welcome to look at the research jupyter notebook.

We tested various recommender systems provided by surprise and these are the results we got:

User-Based CF

FIELD1	RMSE	MSE	MAE	P@5	R@5	F1@5	P@10	R@10	F1@10	P@15	R@15	F1@15
SVD	1.247	1.554	0.951	0.822	0.808	0.815	0.82	0.831	0.826	0.821	0.834	0.827
SVDpp	1.242	1.543	0.945	0.819	0.79	0.804	0.815	0.808	0.812	0.815	0.809	0.812
SlopeOne	1.486	2.209	1.136	0.771	0.684	0.725	0.77	0.697	0.731	0.775	0.701	0.736
NMF	2.165	4.686	1.87	0.282	0.141	0.188	0.286	0.143	0.19	0.28	0.141	0.188
NormalPredictor	2.113	4.465	1.677	0.738	0.617	0.672	0.74	0.628	0.679	0.737	0.625	0.676
KNNBaselineMSD	1.398	1.956	1.067	0.817	0.775	0.796	0.816	0.794	0.805	0.812	0.79	0.801
KNNBaselineCosine	1.387	1.924	1.059	0.821	0.785	0.803	0.816	0.799	0.808	0.816	0.799	0.807
KNNBaselinePearson	1.345	1.808	1.023	0.826	0.828	0.827	0.824	0.851	0.838	0.822	0.853	0.837
KNNBaselinePearsonBaseline	1.362	1.854	1.038	0.825	0.825	0.825	0.823	0.848	0.835	0.823	0.847	0.835
KNNBasicMSD	1.57	2.466	1.197	0.812	0.792	0.802	0.808	0.81	0.809	0.808	0.812	0.81
KNNBasicCosine	1.582	2.502	1.212	0.814	0.805	0.809	0.81	0.821	0.815	0.811	0.823	0.817
KNNBasicPearson	1.599	2.558	1.247	0.812	0.885	0.847	0.812	0.925	0.865	0.812	0.925	0.865
KNNBasicPearsonBaseline	1.612	2.598	1.251	0.811	0.877	0.843	0.811	0.913	0.859	0.811	0.916	0.86
KNNWithMeansMSD	1.376	1.895	1.048	0.787	0.736	0.76	0.786	0.755	0.77	0.788	0.758	0.773
KNNWithMeansCosine	1.356	1.839	1.03	0.787	0.738	0.762	0.786	0.758	0.772	0.786	0.758	0.772
KNNWithMeansPearson	1.415	2.001	1.079	0.733	0.756	0.744	0.735	0.788	0.761	0.733	0.787	0.759
KNNWithMeansPearsonBaseline	1.422	2.023	1.085	0.736	0.752	0.744	0.739	0.782	0.76	0.739	0.783	0.76
KNNWithZscoremsd	1.379	1.901	1.038	0.792	0.745	0.768	0.788	0.762	0.775	0.791	0.763	0.777
KNNWithZscoreCosine	1.353	1.831	1.019	0.795	0.751	0.772	0.792	0.77	0.781	0.792	0.771	0.782
KNNWithZscorePearson	1.41	1.988	1.073	0.737	0.759	0.748	0.737	0.788	0.761	0.738	0.791	0.763
KNNWithZscorePearsonBaseline	1.423	2.025	1.082	0.741	0.756	0.748	0.738	0.782	0.76	0.741	0.786	0.763
BaselineOnly	1.27	1.612	0.971	0.832	0.85	0.841	0.828	0.874	0.85	0.829	0.878	0.853
CoClustering	1.312	1.721	1.006	0.788	0.733	0.76	0.785	0.75	0.767	0.787	0.75	0.768

Item-Based CF

FIELD1	RMSE	MSE	MAE	P@5	R@5	F1@5	P@10	R@10	F1@10	P@15	R@15	F1@15
KNNBaselineMSD	1.366	1.866	1.026	0.791	0.748	0.769	0.793	0.773	0.783	0.791	0.772	0.781
KNNBaselineCosine	1.34	1.795	1.01	0.794	0.755	0.774	0.791	0.775	0.783	0.792	0.778	0.785
KNNBaselinePearson	1.376	1.893	1.038	0.813	0.793	0.803	0.811	0.817	0.814	0.809	0.814	0.811
KNNBaselinePearsonBaseline	1.387	1.923	1.047	0.811	0.787	0.799	0.809	0.81	0.809	0.808	0.809	0.808
KNNBasicMSD	1.519	2.308	1.141	0.779	0.775	0.777	0.778	0.803	0.79	0.777	0.804	0.79
KNNBasicCosine	1.513	2.291	1.14	0.776	0.784	0.78	0.776	0.815	0.795	0.773	0.812	0.792
KNNBasicPearson	1.599	2.557	1.22	0.802	0.851	0.826	0.801	0.885	0.841	0.802	0.887	0.843
KNNBasicPearsonBaseline	1.597	2.551	1.215	0.8	0.838	0.818	0.798	0.871	0.833	0.799	0.872	0.834
KNNWithMeansMSD	1.38	1.905	1.041	0.796	0.732	0.763	0.794	0.75	0.772	0.793	0.75	0.771
KNNWithMeansCosine	1.359	1.848	1.024	0.794	0.734	0.763	0.797	0.756	0.776	0.795	0.757	0.775
KNNWithMeansPearson	1.466	2.148	1.114	0.809	0.765	0.786	0.807	0.782	0.794	0.807	0.783	0.795
KNNWithMeansPearsonBaseline	1.471	2.165	1.116	0.805	0.754	0.778	0.808	0.777	0.792	0.805	0.776	0.79
KNNWithZscoreMSD	1.386	1.922	1.043	0.799	0.735	0.766	0.8	0.758	0.778	0.799	0.757	0.777
KNNWithZscoreCosine	1.364	1.86	1.026	0.801	0.742	0.77	0.8	0.763	0.781	0.8	0.762	0.78
KNNWithZscorePearson	1.47	2.161	1.118	0.808	0.765	0.786	0.81	0.785	0.797	0.809	0.786	0.797
KNNWithZscorePearsonBaseline	1.471	2.165	1.117	0.808	0.764	0.785	0.806	0.779	0.792	0.806	0.779	0.793

Project Setup and Run

1. Clone this repository.
2. Open cmd/shell/terminal and go to project folder: cd AnimeRS
3. Install project dependencies: pip install -r requirements.txt
4. Run the streamlit app: streamlit run ./app/anime_app.py
5. Enjoy the application.

Please let me know if you find bugs or something that needs to be fixed.

Hope you enjoy it.