slimrec-package: slimrec

In talegari/slimrec: Sparse Linear Method to Predict Ratings and Top-N Recommendations

Description

Sparse Linear Method to Predict Ratings and Top-N Reccomendations

Details

Sparse linear method (DOI: 10.1109/ICDM.2011.134) predicts ratings and top-n recommendations suited for sparse implicit positive feedback systems. SLIM is decomposed into multiple elasticnet optimization problems which are solved in parallel over multiple cores. The package is based on "SLIM: Sparse Linear Methods for Top-N Recommender Systems" by Xia Ning and George Karypis.

The method predicts ratings of a user for a given item as a linear combination ratings of all other items provided by the user. The coefficients for an item are determined elastic-net regression (both L1 and L2 regularization) over ratings matrix.

The optimization problem solves:

\min_{c_{j,.}} \frac{1}{2} \|a_{j,.} - Ac_{j,.}\|^2_{2} + \frac{β}{2} \|c_{j,.}\|^2_{2} + γ \|c_{j,.}\|_{1}

subject to c_{j,j} = 0 and optional non-negative constraint c_{j,.} >= 0 where a_{j,.} is the j th column of the input ratings matrix and c_{j,.} is the j th column of the coefficient matrix(to be determined).

The method assumes that unknown rating values to be zero. Hence, it is primarily designed for implicit feeback mechanisms, but not restricted them. The main use of the ratings is to generate top-n lists of users and items.

The package provides three functions:

• slim: Function to compute ratings and coefficient matrix for the sparse ratings matrix using SLIM method.

• tune_slim: Function to arrive at an optimal value of alpha for slim.

• top_rows/cols: Functions to find row/column numbers corresponding the largest values in a particular column/row of a matrix. This is helpful to generate top-n users or items as recommendations.

The package is primarily based on the wonderful package glmnet by Jerome Friedman, Trevor Hastie, Noah Simon, Rob Tibshirani.

If you intend to use SLIM method for large matrices( around >= 1e7 ratings), this package might be slow enough to be practically useful even in parallel mode. You might want to look at biglasso and other implementations like librec.

talegari/slimrec documentation built on May 31, 2019, 2:51 a.m.