Learning to Build User-tag Profile in Recommendation System (UTPM)
User profiling is one of the most important components in recommendation systems, where a user is profiled using demographic (e.g. gender, age, and location) and user behavior information (e.g. browsing and search history). Among different dimensions of user profiling, tagging is an explainable and widely-used representation of user interest. In this paper, we propose a user tag profiling model (UTPM) to study user-tag profiling as a multi-label classification task using deep neural networks. Different from the conventional model, our UTPM model is a multi-head attention mechanism with shared query vectors to learn sparse features across different fields. Besides, we introduce the improved FM-based cross feature layer, which outperforms many state-of-the-art cross feature methods and further enhances model performance. Meanwhile, we design a novel joint method to learn the preference of different tags from a single clicked news article in recommendation systems. Furthermore, our UTPM model is deployed in the WeChat "Top Stories" recommender system, where both online and offline experiments demonstrate the superiority of the proposed model over baseline models.
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