recommender's Introduction

recommender

A collection of recommender systems

Installation:

git clone [email protected]:d704e18/recommender.git
cd recommender
pip install -r requirements.txt

To automatically download the movie dataset from kaggle you need a kaggle api token. Instructions for installing the kaggle API token can be found here: https://github.com/Kaggle/kaggle-api#api-credentials You may also download the dataset manually from https://www.kaggle.com/rounakbanik/the-movies-dataset/version/7/.

Usage

Before you can run the algorithms you need to load a dataset. It is recommended to load ml-1m with recommender load_datset ml-1m, and run algorithms with it: recommender alg mfnonvec ml-1m.

Commands:

General usage:

usage: recommender [-h] [--config CONFIG] {load_dataset,alg} ...

positional arguments:
  {load_dataset,alg}  sub-command help
    load_dataset      load dataset into database
    alg               run recommender algorithms

optional arguments:
  -h, --help          show this help message and exit
  --config CONFIG     configurations to use

Loading datasets:

usage: recommender load_dataset [-h] {ml-26m,ml-1m,ml-100k}

positional arguments:
  {ml-26m,ml-1m,ml-100k}
                        the dataset choices

optional arguments:
  -h, --help            show this help message and exit

Running algorithms:

usage: __main__.py alg [-h] [-s] [-l LOAD]
                       {mfnonvec,mp} {ml-26m,ml-1m,ml-100k} {map} [{map} ...]

positional arguments:
  {mfnonvec,mp}         the recommender algorithm to run
  {ml-26m,ml-1m,ml-100k}
                        the dataset to use
  {map}                 Evaluation methods

optional arguments:
  -h, --help            show this help message and exit
  -s, --save            save the model for later use
  -l LOAD, --load LOAD  load model from given directory

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