Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

1 code implementation • 12 Jul 2021 • Liwei Huang, Yutao Ma, Yanbo Liu, Bohong, Du, Shuliang Wang, Deyi Li

PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator.

Position Sequential Recommendation