Specifically, we propose Coupled Sparse Matrix Factorization (CSMF) to deal with the heterogeneous fusion and data sparsity challenges raised in this problem.
Nowadays, there is an emerging way of connecting logistics orders and van drivers, where it is crucial to predict the order response time.
Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services ; Proceedings of the 2017 ACM on Conference on Information and Knowledge ...
Coupled sparse matrix factorization for response time prediction in logistics services · Department of Computing · The Hong Kong Polytechnic University.
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We then design a coupled sparse matrix factorization (CSMF) model to capture the complex relations among these features. Extensive experiments on real-world ...
IEEE Transactions on Services Computing 11 (2), 399-414, 2018. 7, 2018. Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services.
Aug 16, 2021 · Sun, P.S. Yu, Coupled Sparse Matrix Factorization for Response Time. Prediction in Logistics Services, in: Proceedings of the 2017 ACM on ...
We then design a coupled sparse matrix factorization (CSMF) model to capture the complex relations among these features. Extensive experiments on real-world ...
In order to address these challenges, we formulate joint analysis of data from multiple platforms as a coupled matrix factorization problem with sparsity ...