Metadata-Version: 2.1
Name: recommender-engine
Version: 0.3.0
Summary: A recommendation application using either item-based or user-based approaches
Home-page: https://github.com/tranlyvu/recommender
Author: Tran Ly Vu
Author-email: vutransingapore@gmail.com
Maintainer: Tran Ly Vu <vutransingapore@gmail.com>
Maintainer-email: vutransingapore@gmail.com
License: Apache License 2.0
Download-URL: https://github.com/tranlyvu/recommender/releases
Project-URL: Source, https://github.com/tranlyvu/recommender
Project-URL: Tracker, https://github.com/tranlyvu/recommender/issues
Project-URL: Chat: Gitter, https://gitter.im/recommender/Lobby
Project-URL: CI: Travis, https://travis-ci.org/tranlyvu/recommender
Project-URL: Coverage: coveralls, https://coveralls.io/github/tranlyvu/recommender
Description: <p align="center">
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        </p>
        
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        ---
        
        Recommender is a recommendation application using either item-based or user-based approaches.
        
        Recommender is at version [v0.3.0](https://github.com/tranlyvu/recommender/releases), also see [change log](https://github.com/tranlyvu/recommender/blob/dev/CHANGELOG.md) for more details on release history.
        
        If you like this project, feel fee to leave a few words of appreciation here [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/tranlyvu)
        
        | Build | [![Build Status][3]][4] | [![Coverage Status][5]][6] |
        | :--- | :--- | :---  |
        | **Quality** | [![Maintainability][13]][14] | [![Requirements Status][19]][20] |
        | **Support** | [![gitter][17]][18] |  |
        | **Platform** | [![pyversion][25]][26] | [![implementation][27]][28] |
        
        [3]: https://travis-ci.org/tranlyvu/recommender.svg?branch=dev
        [4]: https://travis-ci.org/tranlyvu/recommender
        [5]: https://coveralls.io/repos/github/tranlyvu/recommender/badge.svg?branch=dev
        [6]: https://coveralls.io/github/tranlyvu/recommender?branch=dev
        [13]: https://api.codeclimate.com/v1/badges/de05d6acb8cd3b11aa0c/maintainability
        [14]: https://codeclimate.com/github/tranlyvu/recommender/maintainability
        [19]: https://requires.io/github/tranlyvu/recommender/requirements.svg?branch=dev
        [20]: https://requires.io/github/tranlyvu/recommender/requirements/?branch=dev
        [17]: https://badges.gitter.im/gitterHQ/gitter.png
        [18]: https://gitter.im/recommender-engine
        [25]: https://img.shields.io/pypi/pyversions/recommender-engine.svg
        [26]: https://pypi.org/project/recommender-engine/
        [27]: https://img.shields.io/pypi/implementation/recommender-engine.svg
        [28]: https://pypi.org/project/recommender-engine/
        
        ---
        Table of contents
        ---
        
        1. [Usage](#Usage)
        2. [Contribution](#Contribution) 
        4. [License](#License)
        
        ---
        Usage
        ---
        
        Install with pip
        
        ```
        $ pip install recommender-engine
        ```
        
        ### API
        
        make_recommendation(person_to_recommend, preference_space, recommender_approach='user_based', number_of_items_to_recommend=10, similarity_measure='euclidean_distance')
        
        ```	
        	Return list of recommendation items based on the chosen approach and similarity emasure
        
        	Parameters
        	--------------
        	person_to_recommend (str): user id/name to recommend to
        
        	preference_space (dict):  keys are user id/name and values are dictionary of items and ratings
        
        	recommender_approach (str): support 'user_based' (default) or 'item_based'
        
        	number_of_items_to_recommend (int): number of items to recommend (default=10)
        
        	similarity_measure (str): similarity measurement method , support 'euclidean_distance' (default), 'cosine' or 'pearson_correlation'
        ```
        
        ### Example
        
        ```
        
        >>> from recommender_engine import make_recommendation
        >>>	result = make_recommendation(person_to_recommend = "userA",
        								 preference_space = preference_space,
        								 recommender_approach = 'user_based',
        								 number_of_items_to_recommend = 10,
        								 similarity_measure = 'euclidean_distance')
        ```
        
        The preference space is dictionary data structure where keys are users and values are dictionary of items and ratings
        
        ```
        preference_space = {
        					'userA : {
        							 'item1' : 'ratingA1, 
        							 'item2' : 'ratingA2',
        							  ..., 
        							  'itemn' : 'ratingAn
        							  }, 
        					..., 
        					'userZ:{
        							'item1' : 'ratingZ1,
        							 'item2' : 'ratingZ2',
        							  ...,
        							 'itemn' : 'ratingZn
        							}
        				    }
        ```
        
        ### Tested Datasets
        
        The project has been tested on these Datasets
        
        1. [Jester](http://goldberg.berkeley.edu/jester-data)
        2. [MovieLens](http://files.grouplens.org/datasets/movielens/)
        
        ---
        Contribution [![Open Source Helpers][7]][8] 
        ---
        [7]: https://www.codetriage.com/tranlyvu/recommender/badges/users.svg
        [8]: https://www.codetriage.com/tranlyvu/recommender
        
        Please follow our contribution convention at [contribution instruction](https://github.com/tranlyvu/recommender/blob/dev/CONTRIBUTING.md) and [code of conduct](https://github.com/tranlyvu/recommender/blob/dev/CODE-OF-CONDUCT.md)
        
        Please check out the [issue file](https://github.com/tranlyvu/recommender/blob/dev/ISSUES.md) for list of issues that required helps.
        
        ### Appreciation
        
        Feel free to add your name into the [list of contributors](https://github.com/tranlyvu/recommender/blob/dev/CONTRIBUTORS.md). You will automatically be inducted into Hall of Fame as a way to show my appreciation for your contributions
        
        #### Hall of Fame
        
        [![](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/images/0)](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/links/0)[![](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/images/1)](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/links/1)[![](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/images/2)](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/links/2)[![](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/images/3)](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/links/3)[![](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/images/4)](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/links/4)[![](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/images/5)](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/links/5)[![](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/images/6)](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/links/6)[![](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/images/7)](https://sourcerer.io/fame/tranlyvu/tranlyvu/recommender/links/7)
        
        ---
        License
        ---
        
        See the [LICENSE](https://github.com/tranlyvu/recommender/blob/master/LICENSE) file for license rights and limitations (Apache License 2.0).
        
        
        
Keywords: Recommender,Artificial Intelligence,Data Science
Platform: any
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: Apache Software License 
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Environment :: Console
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Education
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Framework :: Pytest
Requires-Python: >=2.7, <4
Description-Content-Type: text/markdown
