Binarized Collaborative Filtering with Distilling Graph Convolutional Networks

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.

7 pages, 3 figures,ijcai

References in corpus (6)

• Distilling the Knowledge in a Neural Network
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Deep Convolutional Networks on Graph-Structured Data
• Graph Convolutional Matrix Completion
• Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
• Towards Binary-Valued Gates for Robust LSTM Training

Cited by in corpus (6)

• Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
• Graph Learning Approaches to Recommender Systems: A Review
• Distilling Knowledge from Graph Convolutional Networks
• A Survey of Latent Factor Models in Recommender Systems
• Topology Distillation for Recommender System
• Dual Correction Strategy for Ranking Distillation in Top-N Recommender System