Social media as author-audience games

We present an approach for the prediction of user authorship and feedback behavior with shared content. We consider that users use models of other users and their feedback to choose what to publish next. We look at the problem as a game between authors and audiences and relate it to current content-based user modeling solutions with no prior strategic models. As applications, we consider the large-scale authorship of Wikipedia pages, movies and food recipes. We demonstrate analytic properties, authorship and feedback prediction results, and an overall framework to study content authorship regularities in social media.

User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation

Accurate user modeling is critical for news recommendation. Existing news recommendation methods usually model users' interest from their behaviors via sequential or attentive models. However, they cannot model the rich relatedness between user behaviors, which can provide useful contexts of these behaviors for user interest modeling. In this paper, we propose a novel user modeling approach for news recommendation, which models each user as a personalized heterogeneous graph built from user behaviors to better capture the fine-grained behavior relatedness. In addition, in order to learn user interest embedding from the personalized heterogeneous graph, we propose a novel heterogeneous graph pooling method, which can summarize both node features and graph topology, and be aware of the varied characteristics of different types of nodes. Experiments on large-scale benchmark dataset show the proposed methods can effectively improve the performance of user modeling for news recommendation.