Co-clustering based dual prediction for cargo pricing optimization

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.