This is a very early python implementation of recommender system engine that currently includes 3 types of algorithms:
- item-to-item (with basic items similarity measure)
- SVD-based matrix factorization
- tensor factorization
Additional categorical information (such as movie genre or product category) can be encapsulated into new dimension, s.t. full data is represented as a 3rd order tensor. Encapsulation of that type of information requires additional pre-processing which was described in my talk at TDA 2016 conference and will be also explained in my future paper. Pre-processing is implemented for both matrix (optionally) and tensor factorization and turns the latter into a type of context-aware collaborative filtering approach.
Current version was tested only on Windows x64 with latest anaconda package. Major dependencies are:
- pandas
- numpy
- numba (used only for tensor decomposition)
Important note: Please, be aware, that evaluation of tensor factorization method is performed in batch (e.g. for all test users at once) and requires considerable amount of computer memory. Memory load can be controlled with chunk attribute of the model. General advise is to have PC with >8Gb of RAM.