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.

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

• 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)

• Python
• scipy
• numpy
• pandas
• scikit-learn

For Linux and MAC use:

$ pip install requirements

For Windows use:

http://www.lfd.uci.edu/~gohlke/pythonlibs/#matplotlib

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

For more information about RiVal and the documentation, visit the Case Recommender Wiki. If you have not used Case Recommender before, do check out the Getting Started guide.

Divide Database (Fold Cross Validation)

>> from caserec.utils.split_database import SplitDatabase
>> SplitDatabase(input_file=dataset, dir_folds=dir_path, n_splits=10).kfoldcrossvalidation()             

Run Item Recommendation Algorithm (E.g: ItemKNN)

>> from caserec.recommenders.item_recommendation.itemknn import ItemKNN
>> ItemKNN(train_file, test_file).compute()

Run Rating Prediction Algorithm (E.g: ItemKNN)

>> from caserec.recommenders.rating_prediction.itemknn import ItemKNN
>> ItemKNN(train_file, test_file).compute()

Evaluate Ranking (Prec@N, Recall@N, NDCG@, Map@N and Map Total)

>> from caserec.evaluation.item_recommendation import ItemRecommendationEvaluation
>> ItemRecommendationEvaluation().evaluate_with_files(predictions_file, test_file)

Evaluate Ranking (MAE and RMSE)

>> from caserec.evaluation.rating_prediction import RatingPredictionEvaluation
>> RatingPredictionEvaluation().evaluate_with_files(predictions_file, test_file)

The input-files of traditional have to be placed in the corresponding subdirectory and are in csv-format with at least 3 columns. Example: user_1,item_1,feedback

To help the project with contributions follow the steps:

• Fork CaseRecommender

• Make your alterations and commit

• Create a topic branch - git checkout -b my_branch

• Push to your branch - git push origin my_branch

• Create a Pull Request from your branch.

• You just contributed to the CaseRecommender project!

For bugs or feedback use this link: https://github.com/caserec/CaseRecommender/issues

© 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
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