In many industrial applications like online advertising and recommendation systems, diverse and accurate user profiles can greatly help improve personalization. For building user profiles, deep learning is widely used to mine expressive tags to describe users preferences from their historical actions. For example, tags mined from users click-action history can represent the categories of ads that users are interested in, and they are likely to continue being clicked in the future. Traditional solutions usually introduce multiple independent Two-Tower models to mine tags from different actions, e.g., click, conversion. However, the models cannot learn complementarily and support effective training for data-sparse actions. Besides, limited by the lack of information fusion between the two towers, the model learning is insufficient to represent users preferences on various topics well. This paper introduces a novel multi-task model called Mixture of Virtual-Kernel Experts (MVKE) to learn multiple topic-related user preferences based on different actions unitedly. In MVKE, we propose a concept of Virtual-Kernel Expert, which focuses on modeling one particular facet of the users preference, and all of them learn coordinately. Besides, the gate-based structure used in MVKE builds an information fusion bridge between two towers, improving the models capability much and maintaining high efficiency. We apply the model in Tencent Advertising System, where both online and offline evaluations show that our method has a significant improvement compared with the existing ones and brings about an obvious lift to actual advertising revenue.
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