  The personalized list continuation (PLC) task is to curate the next items to
user-generated lists (ordered sequence of items) in a personalized way. The
main challenge in this task is understanding the ternary relationships among
the interacting entities (users, items, and lists) that the existing works do
not consider. Further, they do not take into account the multi-hop
relationships among entities of the same type. In addition, capturing the
sequential information amongst the items already present in the list also plays
a vital role in determining the next relevant items that get curated.
  In this work, we propose HyperTeNet -- a self-attention hypergraph and
Transformer-based neural network architecture for the personalized list
continuation task to address the challenges mentioned above. We use graph
convolutions to learn the multi-hop relationship among the entities of the same
type and leverage a self-attention-based hypergraph neural network to learn the
ternary relationships among the interacting entities via hyperlink prediction
in a 3-uniform hypergraph. Further, the entity embeddings are shared with a
Transformer-based architecture and are learned through an alternating
optimization procedure. As a result, this network also learns the sequential
information needed to curate the next items to be added to the list.
Experimental results demonstrate that HyperTeNet significantly outperforms the
other state-of-the-art models on real-world datasets. Our implementation and
datasets are available at https://github.com/mvijaikumar/HyperTeNet.
