Enhancing Graph Convolution Network for Novel Recommendation

Graph convolution network based recommendation methods have achieved great success. However, existing graph based methods tend to recommend popular items yet neglect tail ones, which are actually the focus of novel recommendation since they can provide more surprises for users and more profits for enterprises. Furthermore, current novelty oriented methods treat all users equally without considering their personal preference on popular or tail items. In this paper, we enhance graph convolution network with novelty-boosted masking mechanism and personalized negative sampling strategy for novel recommendation. Firstly, we alleviate the popularity bias in graph based methods by obliging the learning process to pay more attention to tail items which are assigned to a larger masking probability. Secondly, we empower the novel recommendation methods with users' personal preference by selecting true negative popular samples. Extensive experimental results on three datasets demonstrate that our method outperforms both graph based and novelty oriented baselines by a large margin in terms of the overall F-measure.