Learning and Reasoning on Graph for Recommendation

Recommendation methods construct predictive models to estimate the likelihood of a user-item interaction. Previous models largely follow a general supervised learning paradigm - treating each interaction as a separate data instance and building a supervised learning model upon the information isolated island. Such paradigm, however, overlook relations among data instances, hence easily resulting in suboptimal performance especially for sparse scenarios. Moreover, due to the black-box nature, most models hardly exhibit the reasons behind a prediction, making the recommendation process opaque to understand. In this tutorial, we revisit the recommendation problem from the perspective of graph learning and reasoning. Common data sources for recommendation can be organized into graphs, such as bipartite user-item interaction graphs, social networks, item knowledge graphs (heterogeneous graphs), among others. Such a graph-based organization connects the isolated data instances and exhibits relationships among instances as high-order connectivities, thereby encoding meaningful patterns for collaborative filtering, content-based filtering, social influence modeling, and knowledgeaware reasoning. Inspired by this, prior studies have incorporated graph analysis (e.g., random walk) and graph learning (e.g., network embedding) into recommender models and achieved great success. Together with the recent success of graph neural networks (GNNs), graph-based models have exhibited the potential to be the technologies for next-generation recommender systems. This tutorial provides a review on graph-based learning methods for recommendation, with special focus on recent developments of GNNs. By introducing this emerging and promising topic in this tutorial, we expect the audience to get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies.