Binarized Collaborative Filtering with Distilling Graph Convolutional Networks

TL;DR

This paper introduces a novel binarized collaborative filtering approach that distills high-order GCN ranking information into binary representations, significantly improving online recommendation efficiency while maintaining competitive accuracy.

Contribution

The paper proposes a new framework converting binary optimization into a continuous problem with stochastic penalty, enabling efficient training of binarized collaborative filtering models.

Findings

Outperforms baseline methods on three real-world datasets.

Efficient training with popular solvers like SGD and Adam.

Maintains competitive recommendation accuracy with binary codes.

Abstract

The efficiency of top-K item recommendation based on implicit feedback are vital to recommender systems in real world, but it is very challenging due to the lack of negative samples and the large number of candidate items. To address the challenges, we firstly introduce an improved Graph Convolutional Network~(GCN) model with high-order feature interaction considered. Then we distill the ranking information derived from GCN into binarized collaborative filtering, which makes use of binary representation to improve the efficiency of online recommendation. However, binary codes are not only hard to be optimized but also likely to incur the loss of information during the training processing. Therefore, we propose a novel framework to convert the binary constrained optimization problem into an equivalent continuous optimization problem with a stochastic penalty. The binarized collaborative filtering model is then easily optimized by many popular solvers like SGD and Adam. The proposed algorithm is finally evaluated on three real-world datasets and shown the superiority to the competing baselines.