HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation

TL;DR

HyperTeNet introduces a novel neural network combining hypergraph and Transformer architectures to improve personalized list continuation by effectively modeling complex relationships and sequential information among users, items, and lists.

Contribution

The paper presents HyperTeNet, a new model that captures ternary and multi-hop relationships using hypergraph neural networks and sequential patterns with Transformers, advancing personalized list continuation methods.

Findings

HyperTeNet outperforms state-of-the-art models on real-world datasets.

The model effectively captures complex entity relationships and sequential information.

Experimental results validate the superiority of HyperTeNet in personalized list curation.

Abstract

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.