  These years much effort has been devoted to improving the accuracy or
relevance of the recommendation system. Diversity, a crucial factor which
measures the dissimilarity among the recommended items, received rather little
scrutiny. Directly related to user satisfaction, diversification is usually
taken into consideration after generating the candidate items. However, this
decoupled design of diversification and candidate generation makes the whole
system suboptimal. In this paper, we aim at pushing the diversification to the
upstream candidate generation stage, with the help of Graph Convolutional
Networks (GCN). Although GCN based recommendation algorithms have shown great
power in modeling complex collaborative filtering effect to improve the
accuracy of recommendation, how diversity changes is ignored in those advanced
works. We propose to perform rebalanced neighbor discovering, category-boosted
negative sampling and adversarial learning on top of GCN. We conduct extensive
experiments on real-world datasets. Experimental results verify the
effectiveness of our proposed method on diversification. Further ablation
studies validate that our proposed method significantly alleviates the
accuracy-diversity dilemma.
