Graph Contextualized Self-Attention Network for Session-based Recommendation

Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieved significant success in various sequence modeling tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences. In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. In GC-SAN, we…

• NN
  National Natural Science Foundation of China

  Awards: 61728205, 61876117, 61876217, 61872258
• PA
  Priority Academic Program Development of Jiangsu Higher Education Institutions

  Award: 61876117