1 research outputs found
Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
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.Comment: 7 pages, 3 figures,ijca