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Title: Coupled sparse matrix factorization for response time prediction in logistics services
Authors: Wang, Y 
Cao, J 
He, L
Li, W 
Sun, L
Yu, PS
Keywords: Coupled matrix factorization
Logistics services
Response time prediction
Sparse matrix factorization
Issue Date: 2017
Publisher: Association for Computing Machinery
Source: International Conference on Information and Knowledge Management, Proceedings, 2017, v. Part F131841, p. 939-947 How to cite?
Abstract: 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.
Description: 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Pan Pacific, Singapore, 6-10 November 2017
ISBN: 9781450349185
DOI: 10.1145/3132847.3132948
Appears in Collections:Conference Paper

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