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
- 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
- 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 >= 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/
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.
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
https://github.com/caserec/CaseRecommender
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