Please use this identifier to cite or link to this item: https://doi.org/10.1145/3336191.3371873
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dc.titleLearning and Reasoning on Graph for Recommendations
dc.contributor.authorXiang Wang
dc.contributor.authorXiangnan He
dc.contributor.authorTat-Seng Chua
dc.date.accessioned2020-05-06T04:15:04Z
dc.date.available2020-05-06T04:15:04Z
dc.date.issued2020-02-03
dc.identifier.citationXiang Wang, Xiangnan He, Tat-Seng Chua (2020-02-03). Learning and Reasoning on Graph for Recommendations. WSDM 2020 : 890-893. ScholarBank@NUS Repository. https://doi.org/10.1145/3336191.3371873
dc.identifier.isbn9781450368223
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/167772
dc.description.abstractRecommendation 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 knowledge-aware 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. ? 2020 Copyright held by the owner/author(s).
dc.subjectGraph learning
dc.subjectGraph neural network
dc.subjectRecommendation
dc.typeConference Paper
dc.contributor.departmentDEPT OF COMPUTER SCIENCE
dc.description.doi10.1145/3336191.3371873
dc.description.sourcetitleWSDM 2020
dc.description.page890-893
dc.grant.idR-252-300-002-490
dc.grant.fundingagencyInfocomm Media Development Authority
dc.grant.fundingagencyNational Research Foundation
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