article-recommender 1.0.1

Create a recommendation engine for articles

Recommendation Engine for Articles

This project was originally part of Udacity's Data Scientist nanodegree. The development notebook included in this repository contained the basic steps to create the methods that inform the Recommender class I created. Although this project was originally meant for IBM Watson's platform, I have generalized the approach to any corpus of articles that ought to be served to users.

The goal of this module is to simplify the creation of a simple recommender system. Anyone with two csv files containing information about the interactions between users and existing articles as well as a description about these articles is able to create a recommendation engine with a few lines of code thanks to this project.

Certain article names weird because of formatting in original doc, normal Specify developed on Mac.

Installation

In order to install this package, simply type pip install article_recommender in the terminal. Then, the main class can be imported by from article_recommender import ArticleRecommender. In order to run everything properly (including the development notebook), the following libraries need to be installed locally:

• numpy
• pandas
• nltk
• sklearn
• re
• matplotlib
• pickle

Structure

Two main folders are included in this repository.

Development Notebook

This folder contains all the files pertaining to the development notebook that forms the basis of this package. This notebook was created as part of Udacity's Data Scientist nanodegree, and all the tests associated were created by them.

project_tests.py: tests developed by Udacity to test the functions created in the development notebook.

top_5.p, top_10.p, top_20.p, user_item_matrix.p: additional documents called in project_tests.py to test our results

Recommendations_with_IBM.ipynb: a Jupyter development notebook which contains all the functions informing the package. The bare bones of this (instructions) were provided by Udacity, but all the code is mine. Running this notebook as is will enable to follow the main steps behind the recommendation engine, as well as test new functionalities

Recommendations_with_IBM.html: an HTML version of the development notebook, in case only the results are of interest

Data

articles_community.csv: this example csv file contains information about the IBM Watson articles (we are particularly interested in the article_id and doc_description fields). This file is leveraged in the development notebook.

user-item-interactions.csv: this example csv file contains information about past interactions between users and articles (we are particularly interested in the user_id and article_id fields). This file isn't leveraged in the development notebook per se but serves as an archetypal example in the code below.

user-item-interactions.csv: this example csv file is very similar to the previous one, the only difference being that users are identified by hashed emails rather than user ID's. This file is leveraged by the development notebook.

ArticleRecommender

This folder contains all the files necessary to create and upload the package to PyPi.

setup.py, dist, article_recommender.egg-info: these files are not of interest to understand the package, they only contain metadata or elements necessary for the upload to PyPi.

Article Recommender

The folder article_recommender is where the main files are included.

license.txt: a license in order to use this software, using a template provided by MIT

setup.cfg: additional metadata information

Usage

Caveats

Credit

License