Speaker: Ezgi Karabulut T\u00fcrkseven (Sabanc\u0131 University)
\nTitle: Bundle Pricing via Learning the Market from Customer Preferences
\nDate & Time: 16 October 2026, 13:30
\nPlace: EA 302
\nAbstract: We study how a seller offering n items through mixed bundling can learn the structure of a multi-segmented market from customer purchase choices alone, without observing valuations or segment membership. Because all 2^n bundles must be priced, generic black-box optimization has to search an O(2^n)-dimensional space, which becomes inefficient as n grows. We propose BLMP (Bundle Pricing via Learning the Market from Customer Preferences), which decomposes bundle-level preference feedback into item-specific data, so that item valuations and segment probabilities are learned in O(n) dimensions. These estimates are then used in a mixed-integer program to compute near-optimal bundle prices. Against CMA-ES and GP-UCB, BLMP\u2019s final solutions stay within 1% of the full-information benchmark under deterministic valuations and within 5% under probabilistic valuations. CMA-ES performs poorly, and GP-UCB\u2019s runtime grows severely with the number of interactions.
\nBio: Ezgi Karabulut T\u00fcrkseven is an assistant professor in the Industrial Engineering program at Sabanc\u0131 University. She holds BSc and MSc degrees from the Industrial Engineering department at Bo\u011fazi\u00e7i University and received her PhD in Operations Research from the Georgia Institute of Technology. Her research lies at the intersection of operations research, machine learning, and optimization, with a particular focus on integrating preference learning into decision-making models. Her recent work explores the use of data-driven approaches in multi-period optimization problems with multiple agents.<\/p>\n","protected":false},"excerpt":{"rendered":"
Speaker: Ezgi Karabulut T\u00fcrkseven (Sabanc\u0131 University) Title: Bundle Pricing via Learning the Market from Customer Preferences Date & Time: 16 October 2026, 13:30 Place: EA 302 Abstract: We study how a seller offering n items through mixed bundling can learn the structure of a multi-segmented market from customer purchase choices [\u2026] <\/p>\n