  Sequential recommendation aims to leverage users' historical behaviors to
predict their next interaction. Existing works have not yet addressed two main
challenges in sequential recommendation. First, user behaviors in their rich
historical sequences are often implicit and noisy preference signals, they
cannot sufficiently reflect users' actual preferences. In addition, users'
dynamic preferences often change rapidly over time, and hence it is difficult
to capture user patterns in their historical sequences. In this work, we
propose a graph neural network model called SURGE (short for SeqUential
Recommendation with Graph neural nEtworks) to address these two issues.
Specifically, SURGE integrates different types of preferences in long-term user
behaviors into clusters in the graph by re-constructing loose item sequences
into tight item-item interest graphs based on metric learning. This helps
explicitly distinguish users' core interests, by forming dense clusters in the
interest graph. Then, we perform cluster-aware and query-aware graph
convolutional propagation and graph pooling on the constructed graph. It
dynamically fuses and extracts users' current activated core interests from
noisy user behavior sequences. We conduct extensive experiments on both public
and proprietary industrial datasets. Experimental results demonstrate
significant performance gains of our proposed method compared to
state-of-the-art methods. Further studies on sequence length confirm that our
method can model long behavioral sequences effectively and efficiently.
