Conference report: CIKM 2019 Learning and Reasoning on Graph for Recommendation

class=”markdown_views prism-atom-one-dark”> Foreword This is a conference report on graphs and recommendations at CIKM 2019 Link: https ://dl.acm.org/doi/10.1145/3357384.3360317 1. Summary Recommendation methods build predictive models to estimate the likelihood of user-item interactions. Previous models have largely followed a common supervised learning paradigm—treating each interaction as a separate data instance and making predictions based on “silos of information.” This approach ignores the relationships between data instances and may lead to poor performance, especially in sparse cases. In addition, models built on individual data instances have difficulty showing the reasons behind recommendations, making the recommendation process difficult to understand. We will revisit the recommendation problem from the perspective of graph learning. Common recommendation data sources can be organized into graphs, such as user-item interactions (bipartite graphs), social networks, knowledge graphs (heterogeneous graphs), etc. This graph-based organization connects isolated data instances, bringing benefits for developing higher-order connections that encode meaningful patterns for collaborative filtering, content-based filtering, social impact modeling, and knowledge-aware reasoning. Coupled with the recent success of graph neural networks (GNNs), graph-based models have the potential to become the next generation of recommendation system technology. And this article reviews graph-based recommendation learning methods, paying special attention to the latest developments in GNNs…