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1Department of Analytics and Operations, NUS Business School, National University of Singapore, Singapore; 2Institute of Operations Research and Analytics, National University of Singapore, Singapore
Our study focuses on a preference learning setting where customers select k most preferred items from a personalized set. Our new Mallows-type ranking model offers simple closed-form (ranked) choice probabilities that can be learned through MLE with theoretical guarantees. We demonstrate the model's excellent performance with real data sets. We use our model to study how feedback structure is related to the efficiency of the feedback collection and find that a little favor goes a long way.
Practical choice estimation from a machine learning perspective
Joohwan Ko1, Andrew Li2
1KAIST, South Korea; 2Carnegie Mellon University, United States of America
This paper applies a machine learning lens to the problem of choice estimation. We (1) establish the first truly realistic-scale benchmark for practical choice estimation, (2) use this benchmark to run the largest evaluation of choice models to date, and (3) propose and prescribe the use of a simple, irrational choice model, which we dub the Sparse Halo-MNL.
Store-Specific Assortments in the Presence of Product Constraints
Mert Cetin, Victor Martinez-de-Albeniz
IESE Business School, Spain
When allocating products to brick-and-mortar stores, retailers face product availability constraints that force them to balance product offerings across stores. We model this problem under multinomial logit demand and show that the problem is NP-complete. We develop a tractable continuous relaxation of the problem which has a unique local maximum and allows us to build near-optimal solution algorithms. We use data from a large retailer and identify improvements of better product-store matching.
