Low-rank matrix factorization with attributes

TL;DR

This paper introduces a novel collaborative filtering method that integrates user preferences with product and user attributes using a generalized low-rank matrix completion framework, improving prediction accuracy.

Contribution

It presents a new generalized matrix completion approach utilizing tensor product kernels to incorporate attribute information into collaborative filtering.

Findings

Outperforms standard matrix completion in movie rating predictions.

Demonstrates advantages over multi-task and single-task learning methods.

Validates effectiveness through benchmark experiments.

Abstract

We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classical function approximation, to predict a given user's interest in a particular product. Our method is a generalized low rank matrix completion problem, where we learn a function whose inputs are pairs of vectors -- the standard low rank matrix completion problem being a special case where the inputs to the function are the row and column indices of the matrix. We solve this generalized matrix completion problem using tensor product kernels for which we also formally generalize standard kernel properties. Benchmark experiments on movie ratings show the advantages of our generalized matrix completion method over the standard matrix completion one with no information about movies or people, as well as over standard multi-task or single task learning methods.