Advertising is critical to many online e-commerce platforms such as e-Bay and
Amazon. One of the important signals that these platforms rely upon is the
click-through rate (CTR) prediction. The recent popularity of multi-modal
sharing platforms such as TikTok has led to an increased interest in online
micro-videos. It is, therefore, useful to consider micro-videos to help a
merchant target micro-video advertising better and find users' favourites to
enhance user experience. Existing works on CTR prediction largely exploit
unimodal content to learn item representations. A relatively minimal effort has
been made to leverage multi-modal information exchange among users and items.
We propose a model to exploit the temporal user-item interactions to guide the
representation learning with multi-modal features, and further predict the user
click rate of the micro-video item. We design a Hypergraph Click-Through Rate
prediction framework (HyperCTR) built upon the hyperedge notion of hypergraph
neural networks, which can yield modal-specific representations of users and
micro-videos to better capture user preferences. We construct a time-aware
user-item bipartite network with multi-modal information and enrich the
representation of each user and item with the generated interests-based user
hypergraph and item hypergraph. Through extensive experiments on three public
datasets, we demonstrate that our proposed model significantly outperforms
various state-of-the-art methods.

使用 Hypergraph Click-Through Rate prediction framework 模型，利用多模态交互的用户和物品信息和生成的基于兴趣的用户超图和物品超图，通过时间感知的用户 - 物品二分网络来预测用户对微视频点击率，该模型表现显著优于其他现有的模型。