Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
This work addresses efficiency and scalability issues in recommender systems for real-world applications, though it is incremental as it builds on existing binarization and GCN techniques.
The paper tackles the challenge of efficient top-K item recommendation in implicit feedback settings by distilling ranking information from a Graph Convolutional Network into a binarized collaborative filtering model, achieving improved efficiency and showing superiority over baselines on three real-world datasets.
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.