Coupled sparse matrix factorization for response time prediction in logistics services

Yuqi Wang, Jiannong Cao, Lifang He, Wengen Li, Lichao Sun, Philip S. Yu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


Nowadays, there is an emerging way of connecting logistics orders and van drivers, where it is crucial to predict the order response time. Accurate prediction of order response time would not only facilitate decision making on order dispatching, but also pave ways for applications such as supply-demand analysis and driver scheduling, leading to high system efficiency. In this work, we forecast order response time on current day by fusing data from order history and driver historical locations. Specifically, we propose Coupled Sparse Matrix Factorization (CSMF) to deal with the heterogeneous fusion and data sparsity challenges raised in this problem. CSMF jointly learns from multiple heterogeneous sparse data through the proposed weight mechanism therein. Experiments on real-world datasets demonstrate the effectiveness of our approach, compared to various baseline methods. The performances of many variants of the proposed method are also presented to show the effectiveness of each component.

Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450349185
Publication statusPublished - 6 Nov 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841


Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017


  • Coupled matrix factorization
  • Logistics services
  • Response time prediction
  • Sparse matrix factorization

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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