[KDD 2020] Dual Channel Hypergraph Collaborative Filtering
Aug 13, 20201 views
Collaborative filtering (CF) is one of the most popular and important recommendation methodologies in the heart of numerous,recommender systems today. Although widely adopted, existing,CF-based methods, ranging from matrix factorization to the emerging graph-based methods, suffer inferior performance especially,when the data for training are very limited. In this paper, we first,pinpoint the root causes of such deficiency and observe two main,disadvantages that stem from the inherent designs of existing CFbased methods,,i.e.,,1),inflexible modeling of users and items,and 2),insufficient modeling of high-order correlations among the subjects,.,Under such circumstances, we propose a dual channel hypergraph,collaborative filtering (,DHCF,) framework to tackle the above issues.,First,, a dual channel learning strategy, which holistically leverages,the divide-and-conquer strategy, is introduced to learn the representation of users and items so that these two types of data can be,elegantly interconnected while still maintaining their specific properties.,Second,, the hypergraph structure is employed for modeling,users and items with explicit hybrid high-order correlations. The,jump hypergraph convolution ( JHConv) method is proposed to support the explicit and efficient embedding propagation of high-order,correlations. Comprehensive experiments on two public benchmarks and two new real-world datasets demonstrate that,DHCF,can achieve significant and consistent improvements against other,state-of-the-art methods.