resype 0.1

recommender system using machine learning

ReSyPE: Recommender System in Python Environment

Introduction

ReSyPE (pronounced recipe) is a Python library built for both practitioners and researchers that wish to quickly develop and deploy ML-based recommender systems.

The library provides an end-to-end pipeline that includes:

1. Loading transaction, user feature, and item feature datasets
2. Interchangable methods for user and item clustering
3. Modular framework for machine learning models
4. Iterative and decomposition-based techniques

Installation

pip install resype

Setup

The package uses Python 3 and virtualenv to manage the environment. Once you've cloned this repo run this from the root of the repo to create the environment:

conda env create -f environment.yml
source activate resype

Getting Started

import pandas as pd
import numpy as np
from resype.collab_filtering import CollabFilteringModel

# load transaction list
transaction_list = pd.read_csv("sample_data/ratings.csv")
transaction_list = transaction_list[['userId', 'movieId', 'rating']]
transaction_list = transaction_list.sample(20)
transaction_list.columns = ["user_id", 'item_id', 'rating']

re = CollabFilteringModel(transaction_list)

# construct utlity matrix
re.construct_utility_matrix()

# import sklearn Model
from sklearn.neural_network import MLPClassifier, MLPRegressor
mlp1 = MLPRegressor(hidden_layer_sizes=(50, 50))

# fit and predict
re.fit(mlp1, method='iterative')

# recommend
user_list = [0, 1, 2] # indices
top_n = 10
re.get_rec(user_list, top_n)

Comparison with Surprise

Features	Surprise	ReSyPE
Handles explicit rating data	✔️	✔️
Cross validation	✔️	✔️
Recommendation evaluation	✔️	✔️
Collaborative filtering	✔️	✔️
Content-based filtering		✔️
Customizable ML models		✔️

Performance

Prediction Algorithm	MSE	MAE
NormalPredictor	2.051080	1.135742
BaselineOnly	0.858667	0.735921
KNNBasic	1.362782	0.906558
KNNWithMeans	1.173480	0.850230
KNNWithZScore	1.185011	0.842193
KNNBaseline	1.057957	0.796983
SVD	0.862225	0.730675
NMF	1.360950	0.921752
SlopeOne	1.222082	0.869121
CoClustering	1.299210	0.900984
ReSyPE (SVD-based Collaborative Filtering using Random Forest)	1.343327	0.907147
ReSyPE (Content-based; Random Forest)	0.955075	0.757576