  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.

Social and Information Networks