Session-based recommender systems aim to improve recommendations in
short-term sessions that can be found across many platforms. A critical
challenge is to accurately model user intent with only limited evidence in
these short sessions. For example, is a flower bouquet being viewed meant as
part of a wedding purchase or for home decoration? Such different perspectives
greatly impact what should be recommended next. Hence, this paper proposes a
novel session-based recommendation system empowered by hypergraph attention
networks. Three unique properties of the proposed approach are: (i) it
constructs a hypergraph for each session to model the item correlations defined
by various contextual windows in the session simultaneously, to uncover item
meanings; (ii) it is equipped with hypergraph attention layers to generate item
embeddings by flexibly aggregating the contextual information from correlated
items in the session; and (iii) it aggregates the dynamic item representations
for each session to infer the general purpose and current need, which is
decoded to infer the next interesting item in the session. Through experiments
on three benchmark datasets, we find the proposed model is effective in
generating informative dynamic item embeddings and providing more accurate
recommendations compared to the state-of-the-art.

本论文提出了一种新的基于超图注意力网络的会话推荐系统，它通过构建每个会话的超图、使用超图注意力层来生成项目嵌入，并聚合每个会话的动态项目表示，从而推断下一个感兴趣的项目。实验结果表明，与最先进技术相比，所提出的模型能够有效生成信息丰富的动态项目嵌入，并提供更准确的推荐。

基于超图注意力网络的会话推荐

Session-based Recommendation with Hypergraph Attention Networks

Text classification is a critical research topic with broad applications in
natural language processing. Recently, graph neural networks (GNNs) have
received increasing attention in the research community and demonstrated their
promising results on this canonical task. Despite the success, their
performance could be largely jeopardized in practice since they are: (1) unable
to capture high-order interaction between words; (2) inefficient to handle
large datasets and new documents. To address those issues, in this paper, we
propose a principled model -- hypergraph attention networks (HyperGAT), which
can obtain more expressive power with less computational consumption for text
representation learning. Extensive experiments on various benchmark datasets
demonstrate the efficacy of the proposed approach on the text classification
task.

本论文提出了一种基于超图注意力网络的模型 ——HyperGAT，旨在解决文本分类中词语高阶交互捕捉问题和处理大型数据集和新文档的问题，并在多个基准数据集上进行了广泛的实验证明了该方法的有效性。