Meta-Learned User Preference for Topic Participation Prediction

Predicting the potential user interest on topics in online social networks is important for many practical applications such as advertising, recommendation and malicious account identification. Previous methods on such topic prediction problem mainly focus on learning user preference from historical posting content, and/or rely on the interest of friends to infer the topics a user may be interested in. However, these methods fail to take full advantage of high-order interactions between users and topics and the implicit relations among users, which may result in limited performance. In addition, existing approaches usually require a large amount of samples to train the model and therefore have poor prediction performance for the users who have few content and/or rarely follow the topics. To overcome these limitations, we present a novel method MetaTP (Meta learning based Topic Prediction) for exploiting the complex preference of users over the topics and identify the potential topics for cold-start users. MetaTP is built on a fast graph convolutional network to estimate the user interest through extracting user posting behavior from historical posting content and recursively aggregating the interest from the social friends of a user. Moreover, MetaTP introduces a new way of training prediction model in a meta-learning manner, which not only improves the performance on topic prediction but also can effectively and efficiently adapt to users with a few records. We validate our MetaTP model on real-world datasets crawled from popular social platforms and the empirical results show that our approach significantly outperforms the state-of-the-art baselines.