TY - GEN
T1 - Coupled sparse matrix factorization for response time prediction in logistics services
AU - Wang, Yuqi
AU - Cao, Jiannong
AU - He, Lifang
AU - Li, Wengen
AU - Sun, Lichao
AU - Yu, Philip S.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - 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 se.ing 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.
AB - 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 se.ing 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.
KW - Coupled matrix factorization
KW - Logistics services
KW - Response time prediction
KW - Sparse matrix factorization
UR - http://www.scopus.com/inward/record.url?scp=85037330763&partnerID=8YFLogxK
U2 - 10.1145/3132847.3132948
DO - 10.1145/3132847.3132948
M3 - Conference article published in proceeding or book
AN - SCOPUS:85037330763
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 939
EP - 947
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Y2 - 6 November 2017 through 10 November 2017
ER -