Metadata-Version: 2.1
Name: article_recommender
Version: 1.0.1
Summary: Create a recommendation engine for articles
Home-page: UNKNOWN
Author: Celestin Hermez
Author-email: celestinhermez@gmail.com
License: UNKNOWN
Description: # 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
Platform: UNKNOWN
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