Metadata-Version: 1.1
Name: CaseRecommender
Version: 1.0.14
Summary: A recommender systems framework for Python
Home-page: https://github.com/caserec/CaseRecommender
Author: Arthur Fortes <fortes.arthur@gmail.com>
Author-email: fortes.arthur@gmail.com
License: MIT License
Download-URL: https://github.com/caserec/CaseRecommender/archive/master.zip
Description: Case Recommender - A Python Framework for RecSys
        ===================================================
        
        Case Recommender is a Python implementation of a number of popular recommendation algorithms for both implicit and
        explicit feedback.  The framework aims to provide a rich set of components from which you can construct a customized
        recommender system from a set of algorithms. Case Recommender has different types of item recommendation and rating
        prediction approaches, and different metrics validation and evaluation.
        
        Algorithms
        ^^^^^^^^^^^^
        
        Item Recommendation:
        
        - BPRMF
        
        - ItemKNN
        
        - Item Attribute KNN
        
        - UserKNN
        
        - User Attribute KNN
        
        - Group-based (Clustering-based algorithm)
        
        - Paco Recommender (Co-Clustering-based algorithm)
        
        - Most Popular
        
        - Random
        
        - Content Based
        
        Rating Prediction:
        
        - Matrix Factorization (with and without baseline)
        
        - SVD
        
        - SVD++
        
        - ItemKNN
        
        - Item Attribute KNN
        
        - UserKNN
        
        - User Attribute KNN
        
        - Item NSVD1 (with and without Batch)
        
        - User NSVD1 (with and without Batch)
        
        - Most Popular
        
        - Random
        
        - gSVD++
        
        - Item-MSMF
        
        Clustering:
        
        - PaCo: EntroPy Anomalies in Co-Clustering
        
        - k-medoids
        
        Evaluation and Validation Metrics
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        - All-but-one Protocol
        
        - Cross-fold- Validation
        
        - Item Recommendation: Precision, Recall, NDCG and Map
        
        - Rating Prediction: MAE and RMSE
        
        - Statistical Analysis (T-test and Wilcoxon)
        
        Requirements
        ^^^^^^^^^^^^^
        
        - Python >= 3
        - scipy
        - numpy
        - pandas
        - scikit-learn
        
        For Linux, Windows and MAC use:
        
            $ pip install requirements
        
        For Windows libraries help use:
        
            http://www.lfd.uci.edu/~gohlke/pythonlibs/#matplotlib
        
        Quick Start and Guide
        ^^^^^^^^^^^^^^^^^^^^^^
        
        For more information about RiVal and the documentation, 
        visit the Case Recommender 
        `Wiki <https://github.com/caserec/CaseRecommender/wiki>`_. If you have not used Case Recommender before, do check out the Getting Started guide.
        
        
        Installation
        ^^^^^^^^^^^^^
        
        Case Recommender can be installed using pip:
        
            $ pip install caserecommender
        
        If you want to run the latest version of the code, you can install from git:
        
            $ pip install -U git+git://github.com/caserec/CaseRecommender.git
        
        More Details
        ^^^^^^^^^^^^^
        
            `https://github.com/caserec/CaseRecommender <https://github.com/caserec/CaseRecommender>`_
        
        
        License (MIT)
        ^^^^^^^^^^^^^^
        
            © 2018. Case Recommender All Rights Reserved
        
            Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
            documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
            rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
            permit persons to whom the Software is furnished to do so, subject to the following conditions:
        
            The above copyright notice and this permission notice shall be included in all copies or substantial portions
            of the Software.
        
            THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
            TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
            THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
            OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
            DEALINGS IN THE SOFTWARE.
        
Keywords: recommender systems,framework,collaborative filtering,content-based filtering,recommendation
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
