A Simple Recommender System with Observed Attributes and Time-Varying Parameters

This package implements a recommender system, similar to matrix factorization-based algorithms (SVD) available in the excellent Surprise.

This package extends the standard SVD recommender system by allowing researchers to include observed items attributes and also user taste parameters that vary over time. The model is fit using stochastic gradient descent.

Getting Started

The easiest way to obtain the package is to install using pip:

pip install recommendx

The following resources may be helpful to use in getting started using recommendx.

1. Documentation.pdf - contains documentation and technical details, including information on installation, methods, and class attributes
2. Demonstration.ipynb - this Jupyter notebook provides a brief tutorial on using recommendx

Recommendation Algorithms

The package contains two recommendation methods: RWR and RWT.

Recommendation with Regressors (RWR)

RWR implements a slightly modified version of what might we might call the "classic" SVD algorithm. This is often attributed to Simon Funk, who famously used it during the Netflix Prize competition. The classic SVD approach relies only upon latent item attributes. RWR extends this framework by allowing the researcher to specify observed item attributes, as well.

Sample Syntax

from recommendx import RWR
rwr = RWR(n_factors = 5)
rwr.fit(dat,att)
rwr.accuracy(dat,att)
rwr.predict('userA','item10')

Recommendation with Time (RWT)

RWT implements the same basic model as RWR but allows for time-varying taste parameters.

Sample Syntax

from recommendx import RWT
rwt = RWT(n_factors = 4)
rwt.fit(dat,att)
rwt.accuracy(dat,att)
rwt.predict('userA','item10','PM')
rwt.predict('userA','item10','AM')