Recommender_System

Instructions

Enviroment

we used

python version：3.8

Dependencies

you can install all the dependencies by

pip install -r requirements.txt

There may occure some errors with scikit-surprise and faiss, but it is only used in the comparision part for svd optimisation. It won't affect for the use of the main program. So it is okay that you do NOT install it if you only want to play with the basic implementation of out recommender system :)

With pip:

pip install numpy

pip install scikit-surprise

With conda:

conda install -c conda-forge scikit-surprise

conda install -c pytorch faiss-cpu

Usage

you can just run

python run.py

It will lanuch a web page, you can input the user_id

and there are two recommender methods you can choose content_based or collacorative_filtering

choose the the number of k for top k movies

Then you will get the result.

You can continue to play with it by input yes

Code structure

├── data_processing         //data processing fuctions
│   ├── __init__.py
│   ├── embeddings.py
│   └── preprocessing.py
├── dataset                //dataset that we used including the pre-saved matrix
│   ├── ml-latest-small
│   └── saved_embeddings
│       ├── movies_tfidf_embeddings.pkl
│       └── use_rating_matrix_embeddings.pkl
├── evaluations           // The evaluations scripts we used
│   ├── __init__.py
│   ├── p_top_k_evaluation_script.py
│   └── rmse_evaluation_script.py
├── recommender_system    // main functions of this project are here
│   ├── __init__.py
│   ├── collaborative_filtering.py
│   ├── content_based.py
│   ├── evaluation.py
│   └── optimization      //The optimization methods we used
│       ├── __init__.py
│       ├── dimensionality_reduction.py
│       ├── faiss_retrieval.py
│       └── lsh_retrieval.py

├── test                    //Unit tests for the functions
│   ├── __init__.py
│   ├── test_collaborative_filtering.py
│   ├── test_content_based.py
│   ├── test_data_processing.py
│   └── test_evaluation.py
└── web                    //A simple web demo for play with the results
    ├── __init__.py
    └── recommender_web.py
├── requirements.txt      
├── run.py                 //main function to run this program
├── ext.py                 //ext tools to initilize some variables
├── config.json            //config files
├── config.py
├── data_analysis.ipynb    //some analysis of the input data

Credits

Yihan Zhong