Please use this identifier to cite or link to this item: https://doi.org/10.1145/3357384.3360317
Title: Learning and Reasoning on Graph for Recommendation
Authors: Xiang Wang 
Xiangnan He
Tat-Seng Chua 
Keywords: Graph Learning
Graph Neural Network
Recommendation
Issue Date: 3-Nov-2019
Citation: Xiang Wang, Xiangnan He, Tat-Seng Chua (2019-11-03). Learning and Reasoning on Graph for Recommendation. CIKM 2019 : 2971-2972. ScholarBank@NUS Repository. https://doi.org/10.1145/3357384.3360317
Abstract: 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 performing prediction based on the ?information isolated island?. Such methods, however, overlook the relations among data instances, which may result in suboptimal performance especially for sparse scenarios. Moreover, the models built on a separate data instance only can hardly exhibit the reasons behind a recommendation, making the recommendation process opaque to understand. In this tutorial, we revisit the recommendation problem from the perspective of graph learning. Common data sources for recommendation can be organized into graphs, such as user-item interactions (bipartite graphs), social networks, item knowledge graphs (heterogeneous graphs), among others. Such a graph-based organization connects the isolated data instances, bringing benefits to exploiting high-order connectivities that encode meaningful patterns for collaborative filtering, content-based filtering, social influence modeling and knowledge-aware reasoning. Together with the recent success of graph neural networks (GNNs), graph-based models have exhibited the potential to be the technologies for next-generation recommendation systems. This tutorial provides a review on graph-based learning methods for recommendation, with special focus on recent developments of GNNs and knowledge graph-enhanced recommendation. 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. ? 2019 Association for Computing Machinery.
Source Title: CIKM 2019
URI: https://scholarbank.nus.edu.sg/handle/10635/167771
ISBN: 9781450369763
DOI: 10.1145/3357384.3360317
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