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- W4225577943 abstract "AbstractCommunity detection is a well-studied problem in machine learning and recommendation systems literature. In this paper, we study a novel variant of this problem where we assign predefined fashion communities to users in an Ecommerce ecosystem for downstream tasks. We model our problem as a link prediction task in knowledge graphs with multiple types of edges and multiple types of nodes depicting the intricate Ecommerce ecosystems. We employ Relational Graph Convolutional Networks (R-GCN) on top of this knowledge graph to determine whether a user should be assigned to a given community or not. We conduct empirical experiments on two real-world datasets from a leading fashion retailer. Our experiments demonstrate that the proposed graph-based approach performs significantly better than the non-graph-based baseline, indicating that higher order methods like GCN can improve the task of community assignment for fashion and Ecommerce users." @default.
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- W4225577943 date "2022-01-01" @default.
- W4225577943 modified "2023-09-24" @default.
- W4225577943 title "Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users" @default.
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- W4225577943 doi "https://doi.org/10.1007/978-3-030-94016-4_1" @default.
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