HyCoNN: Hybrid Cooperative Neural Networks for Personalized News Discussion Recommendation Julian Risch, Victor Kunstler,¨ Ralf Krestel Hasso Plattner Institute, University of Potsdam Potsdam, Germany
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[email protected] Abstract—Many online news platforms provide comment sec- Since commenting is a highly social activity, we build on tions for reader discussions below articles. While users of the idea of homophily (the tendency of users to associate with these platforms often read comments, only a minority of them people who appear similar). We leverage node2vec to learn regularly write comments. To encourage and foster more frequent engagement, we study the task of personalized recommendation user embeddings on a bipartite graph that connects platform of reader discussions to users. We present a neural network model users with the discussions they participated in. Consequently, that jointly learns embeddings for users and comments encoding pairs of users who often co-occur in reader discussions because general properties. Based on explicit and implicit user feedback, of rivalry, friendship, or shared interests appear closer to each we sample relevant and irrelevant reader discussions to build a other in the embedding space. representative training dataset. We compare to several baselines and state-of-the-art approaches in an evaluation on two datasets In summary, we make the following contributions: First, from The Guardian and Daily Mail. Experimental results show we present HyCoNN (Hybrid Cooperative Neural Networks), that the recommendations of our approach are superior in terms a model that jointly learns representations of users and reader of precision and recall.