CaseRecommender 1.1.1
A recommender systems framework for Python
License: MIT License
Keywords: recommender systems,framework,collaborative filtering,content-based filtering,recommendation

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

- Non-negative Matrix Factorization

- 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

- (E)CoRec

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/

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)
^^^^^^^^^^^^^^

    © 2019. 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.