8 Citations (Scopus)

Abstract

This paper targets the problem of cargo pricing optimization in the air cargo business. Given the features associated with a pair of origination and destination, how can we simultaneously predict both the optimal price for the bid stage and the outcome of the transaction (win rate) in the decision stage? In addition, it is often the case that the matrix representing pairs of originations and destinations has a block structure, i.e., the originations and destinations can be co-clustered such that the predictive models are similar within the same co-cluster, and exhibit significant variation among different co-clusters. How can we uncover the co-clusters of originations and destinations while constructing the dual predictive models for the two stages? We take the first step at addressing these problems. In particular, we propose a probabilistic framework to simultaneously construct dual predictive models and uncover the co-clusters of originations and destinations. It maximizes the conditional probability of observing the responses from both the quotation stage and the decision stage, given the features and the co-clusters. By introducing an auxiliary distribution based on the co-clustering assumption, such conditional probability can be converted into an objective function. To minimize the objective function, we propose the COCOA algorithm, which will generate both the suite of predictive models for all the pairs of originations and destinations, as well as the co-clusters consisting of similar pairs. Experimental results on both synthetic data and real data from cargo price bidding demonstrate the effectiveness and efficiency of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1583-1592
Number of pages10
Volume2015-August
ISBN (Print)9781450336642
DOIs
StatePublished - Aug 10 2015
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: Aug 10 2015Aug 13 2015

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period8/10/158/13/15

Fingerprint

Costs
Air
Industry

Keywords

  • Co-clustering
  • Dual predictive models

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Zhu, Y., Yang, H., & He, J. (2015). Co-clustering based dual prediction for cargo pricing optimization. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 1583-1592). Association for Computing Machinery. https://doi.org/10.1145/2783258.2783337

Co-clustering based dual prediction for cargo pricing optimization. / Zhu, Yada; Yang, Hongxia; He, Jingrui.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August Association for Computing Machinery, 2015. p. 1583-1592.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhu, Y, Yang, H & He, J 2015, Co-clustering based dual prediction for cargo pricing optimization. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. 2015-August, Association for Computing Machinery, pp. 1583-1592, 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015, Sydney, Australia, 8/10/15. https://doi.org/10.1145/2783258.2783337
Zhu Y, Yang H, He J. Co-clustering based dual prediction for cargo pricing optimization. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August. Association for Computing Machinery. 2015. p. 1583-1592 https://doi.org/10.1145/2783258.2783337
Zhu, Yada ; Yang, Hongxia ; He, Jingrui. / Co-clustering based dual prediction for cargo pricing optimization. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August Association for Computing Machinery, 2015. pp. 1583-1592
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