results for au:Berg_R in:stat
In this paper we revisit matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges representing observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. This framework can be viewed as an important first step towards end-to-end learning in settings where the interaction data is integrated into larger graphs such as social networks or knowledge graphs, circumventing the need for multistage frameworks. Our model achieves competitive performance on standard collaborative filtering benchmarks, significantly outperforming related methods in a recommendation task with side information.
Knowledge bases play a crucial role in many applications, for example question answering and information retrieval. Despite the great effort invested in creating and maintaining them, even the largest representatives (e.g., Yago, DBPedia or Wikidata) are highly incomplete. We introduce relational graph convolutional networks (R-GCNs) and apply them to two standard knowledge base completion tasks: link prediction (recovery of missing facts, i.e.~subject-predicate-object triples) and entity classification (recovery of missing attributes of entities). R-GCNs are a generalization of graph convolutional networks, a recent class of neural networks operating on graphs, and are developed specifically to deal with highly multi-relational data, characteristic of realistic knowledge bases. Our methods achieve competitive performance on standard benchmarks for both tasks, demonstrating especially promising results on the challenging FB15k-237 subset of Freebase.