Deep Spatio-Temporal Attention Network for Click-Through Rate Prediction

In online advertising systems, predicting the click-through rate (CTR) is an important task. Many studies only consider targeted advertisements in isolation, but do not focus on its relationship with other ads that may affect the CTR. We look at a variety of additional elements that can help with CTR prediction for tailored advertisements. We consider supplementary ads from two different angles: 1) the spatial domain, where contextual adverts on the same page as the target advertisements are considered and 2) from the perspective of the temporal component, where we assume people have previously clicked unclicked advertisements. The intuition is that contextual ads shown with targeted ads may influence each other. Also, advertisements that are connected reflect user preferences. Ads that are not connected may indicate to some extent what the user is not interested in. We propose a deep spatio-temporal neural network (DSTAN) for CTR prediction to use these auxiliary data effectively. Our model can decrease the noise in new data, learn the interaction between varied extra data and targeted advertisements, and fuse heterogeneous data into a coherent framework, highlighting important hidden information. Offline experiments on two public datasets show that DSTAN outperforms several of the most common methods in CTR prediction.