Fast Matrix Factorization for Online Recommendation with Implicit Feedback

TL;DR

This paper introduces an efficient and effective matrix factorization method for online recommendation systems with implicit feedback, addressing issues of weighting missing data and model updating in dynamic environments.

Contribution

It proposes a novel non-uniform weighting scheme based on item popularity and a new eALS algorithm for fast, incremental model updates in online recommendation settings.

Findings

eALS outperforms existing implicit MF methods in accuracy.

The method enables real-time model updates with new feedback.

Experiments validate efficiency and effectiveness on public datasets.

Abstract

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.