Representation Learning for Predicting Customer Orders

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationKDD ’21
Subtitle of host publicationProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages3735–3744
ISBN (Print)9781450383325
Publication statusPublished - Aug 2021

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Title27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021)
LocationVirtual
PlaceSingapore
Period14 - 18 August 2021

Abstract

The ability to predict future customer orders is of significant value to retailers in making many crucial operational decisions. Different from next basket prediction or temporal set prediction, which focuses on predicting a subset of items for a single user, this paper aims for the distributional information of future orders, i.e., the possible subsets of items and their frequencies (probabilities), which is required for decisions such as assortment selection for front-end warehouses and capacity evaluation for fulfillment centers. Based on key statistics of a real order dataset from Tmall supermarket, we show the challenges of order prediction. Motivated by our analysis that biased models of order distribution can still help improve the quality of order prediction, we design a generative model to capture the order distribution for customer order prediction. Our model utilizes representation learning to embed items into a Euclidean space and design a highly efficient SGD algorithm to learn the item embeddings. Future order prediction is done by calibrating orders obtained by random walks over the embedding graph. The experiments show that our model outperforms all the existing methods. The benefit of our model is also illustrated with an application to assortment selection for front-end warehouses.

Research Area(s)

  • Choice Model, Representation Learning, Random Walk, E-commerce

Citation Format(s)

Representation Learning for Predicting Customer Orders. / Wu, Tongwen; Yang, Yu; Li, Yanzhi; Mao, Huiqiang; Li, Liming; Wang, Xiaoqing; Deng, Yuming.

KDD ’21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York : Association for Computing Machinery, 2021. p. 3735–3744 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review