Feature-Based Matrix Factorization

TL;DR

This paper presents a flexible feature-based matrix factorization approach for recommender systems that easily incorporates new information through features, demonstrated by achieving top performance in a competitive benchmark.

Contribution

It introduces a versatile matrix factorization framework that integrates diverse features without code changes, simplifying model adaptation and improving performance.

Findings

Achieved the best single model on KDDCup'11 track 1

Model easily incorporates new features without code modifications

Demonstrates flexibility and effectiveness in recommender systems

Abstract

Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.