Paper Info
Title
Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services.
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 setting 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.
Year
DOI
Venue
2017
10.1145/3132847.3132948
CIKM
Keywords
Field
DocType
Response time prediction, Coupled matrix factorization, Sparse matrix factorization, Logistics services
Data mining,Sparse matrix factorization,Incomplete Cholesky factorization,Computer science,Scheduling (computing),Response time,Non-negative matrix factorization,Incomplete LU factorization,Sparse matrix
Conference
ISBN
Citations 
PageRank 
978-1-4503-4918-5
0
0.34
References 
Authors
11
6
Name
Order
Citations
PageRank
Yuqi Wang191.24
Jiannong Cao25226425.12
Lifang He336932.74
Wengen Li463.87
Lichao Sun59414.15
Philip S. Yu6306703474.16