Budhiraja, A and Sukhwani, M and Aggarwal, M and Shevade, S and Sathyanarayana, G and Tallamraju, RB (2022) Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users. In: 3rd workshop on recommender systems in fashion and retail, 2021, 27 September - 1 October 2021, Amsterdam, pp. 3-13.
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Community 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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Lecture Notes in Electrical Engineering |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH |
| Keywords: | Ecosystems; Electronic commerce; Graphic methods; Knowledge graph, Community detection; Community OR; Convolutional networks; Down-stream; E-commerce ecosystems; Graph-based; Knowledge graphs; Link prediction; Prediction tasks; Relational graph, Convolution |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 23 May 2022 06:01 |
| Last Modified: | 23 May 2022 06:01 |
| URI: | https://eprints.iisc.ac.in/id/eprint/71863 |
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