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DEPRECATED This is the official repository for the 2017 Recommender Systems course at Polimi.

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MaurizioFD/RecSys_Course_2017

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RecSys Course 2017

This is the official repository for the 2017 Recommender Systems course at Polimi.

This repo is obsolete, please refer to the updated version HERE

This repo contains a Cython implementation of:

  • SLIM BPR: Uses a Cython tree-based sparse matrix, suitable for datasets whose number of items is too big for the dense similarity matrix to fit in memory. Dense similarity is also supported.
  • MF BPR: Matrix factorization optimizing BPR
  • FunkSVD: Matrix factorization optimizing RMSE
  • AsymmetricSVD

This repo contains a Python implementation of:

  • Item-based KNN collaborative
  • Item-based KNN content
  • User-based KNN
  • SLIM_RMSE: SLIM solver using ElasticNet. The solver fits every column in the similarity matrix independently

This repo also provides an implementation of:

  • Cosine Similarity, Adjusted Cosine, Pearson Correlation, Jaccard Correlation, Tanimoto Coefficient: Implemented both in Python and Cython with the same interface, Base.cosine_similarity and Base.Cython.cosine_similarity
  • MAP, recall, precision, ROC-AUC, MRR, RR, NDCG to be used in testing
  • Movielens10MReader: reads movielens 10M rating file, splits it into three URMs for train, test and validation.

Cython code is already compiled for Linux. To recompile the code just set the recompile_cython flag to True. For other OS such as Windows the c-imported numpy interface might be different (e.g. return tipe long long insead of long) therefore the code could require modifications in oder to compile.

In "all_algorithms.py" you can see how to use every model.

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DEPRECATED This is the official repository for the 2017 Recommender Systems course at Polimi.

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