Timezone: »

 
Deoscillated Adaptive Graph Collaborative Filtering
Zhiwei Liu · Lin Meng · Fei Jiang · Jiawei Zhang · Philip Yu

Fri Jul 22 01:45 PM -- 03:00 PM (PDT) @

Collaborative Filtering~(CF) signals are crucial for a Recommender System~(RS) model to learn user and item embeddings. High-order information can alleviate the cold-start issue of CF-based methods, which is modeled through propagating the information over the user-item bipartite graph. Recent Graph Neural Networks~(GNNs) propose to stack multiple aggregation layers to propagate high-order signals. However, there are three challenges, the oscillation problem, varying locality of bipartite graphs, and the fixed propagation pattern, which spoil the ability of the multi-layer structure to propagate information.In this paper, we theoretically prove the existence and boundary of the oscillation problem, and empirically study the varying locality and layer-fixed propagation problems. We propose a new RS model, named as \textbf{D}eoscillated adaptive \textbf{G}raph \textbf{C}ollaborative \textbf{F}iltering~(DGCF), which is constituted by stacking multiple CHP layers and LA layers.We conduct extensive experiments on real-world datasets to verify the effectiveness of DGCF. Detailed analyses indicate that DGCF solves oscillation problems, adaptively learns local factors, and has layer-wise propagation patterns.

Author Information

Zhiwei Liu (University of Illinois, Chicago)
Lin Meng (Florida State University)
Fei Jiang (University of Chicago)
Jiawei Zhang (Florida State University)
Philip Yu (UIC)

More from the Same Authors