We propose a "Learning to Select" problem that selects the best among the flexible size candidates. This makes decisions based not only on the properties of the candidate, but also on the environment in which they belong to. For example, job dispatching in the manufacturing factory is a typical "Learning to Select" problem. We propose Variable-Length CNN which combines the classification power using hidden features from CNN and the idea of flexible input from Learning to Rank algorithms. This not only can handles flexible candidates using Dynamic Computation Graph, but also is computationally efficient because it only builds a network with the necessary sizes to fit the situation. We applied the algorithm to the job dispatching problem which uses the dispatching log data obtained from the virtual fine-tuned factory. Our proposed algorithm shows considerably better performance than other comparable algorithms.
The paper proposed a new framework called `Learning to select’, in which a best candidate needs to be identified in the decision making process such as job dispatching. A CNN architecture is designed, called `Variable-Length CNN’, to solve this problem.
My major concern is on the definition of the proposed concept of `learning-to-select’. Essentially, I’ve not seen its key difference from the classification problem. While `even in the case of completely identical candidates, the label can be 1 in some situations, and in some other situations the label can be 0’, why not including such `situations’ into your feature vector (i.e., x)? Once you do it, the gap between learning to select and classification will vanish. If this is not doable, you should better make more discussions, especially on what the so-called `situations’ are.  Furthermore, the application scope of the proposed framework is not very well discussed. If it is restricted to job dispatching scenarios, why do we need a new concept “learning to select”?
The proposed model looks quite straightforward. Standard CNN is able to capture the variable length input as is done in many NLP tasks. Dynamic computational graph is not new either. In this sense, the technical novelty of this work is somehow limited.
The experiments are weak in that the data are simulated and the baselines are not strong. I’ve not gained enough insights on why the proposed model could outperform the alternative approaches. More discussions and case studies are sorely needed.