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Click-through rate prediction plays an important role in many fields, such as recommender and advertising systems. It is one of the crucial parts to improve user experience and increase industry revenue. Recently, several deep learning-based models are successfully applied to this area. Some existing studies further model user representation based on user historical behavior sequence, in order to capture dynamic and evolving interests. We observe that users usually have multiple interests at a time and the latent dominant interest is expressed by the behavior. The switch of latent dominant interest results in the behavior changes. Thus, modeling and tracking latent multiple interests would be beneficial. In this paper, we propose a novel method named as Deep Multi-Interest Network (DMIN) which models user's latent multiple interests for click-through rate prediction task. Specifically, we design a Behavior Refiner Layer using multi-head self-attention to capture better user historical item representations. Then the Multi-Interest Extractor Layer is applied to extract multiple user interests. We evaluate our method on three real-world datasets. Experimental results show that the proposed DMIN outperforms various state-of-the-art baselines in terms of click-through rate prediction task.

Graph embedding has attracted many research interests. Existing works mainly focus on static homogeneous/heterogeneous networks or dynamic homogeneous networks. However, dynamic heterogeneous networks are more ubiquitous in reality, e.g. social network, e-commerce network, citation network, etc. There is still a lack of research on dynamic heterogeneous graph embedding. In this paper, we propose a novel dynamic heterogeneous graph embedding method using hierarchical attentions (DyHAN) that learns node embeddings leveraging both structural heterogeneity and temporal evolution. We evaluate our method on three real-world datasets. The results show that DyHAN outperforms various state-of-the-art baselines in terms of link prediction task.