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 highorder 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