Jointly Learning Prices and Product Features
Authors: Ehsan Emamjomeh-Zadeh, Renato Paes Leme, Jon Schneider, Balasubramanian Sivan
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study this problem from the viewpoint of online learning: a firm repeatedly interacts with a buyer by choosing a product configuration as well as a price and observing the buyer s purchasing decision. The goal of the firm is to maximize revenue throughout the course of T rounds by learning the buyer s preferences. We study both the case of a set of discrete products and the case of a continuous set of allowable product features. In both cases we provide nearly tight upper and lower regret bounds. |
| Researcher Affiliation | Industry | Ehsan Emamjomeh-Zadeh1 , Renato Paes Leme2 , Jon Schneider2 , Balasubramanian Sivan2 1Facebook Research 2Google Research ehsanez@fb.com, {renatoppl, jschnei, balusivan}@google.com |
| Pseudocode | No | The paper describes algorithms in text (e.g., "Kleinberg-Leighton (KL) Algorithm for One Item", "First Algorithm: Parallelization of the Kleinberg-Leighton Algorithm") but does not provide them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not include any statement about releasing source code for the described methodology or provide a link to a code repository. |
| Open Datasets | No | The paper describes theoretical models and algorithms and does not involve empirical training on a dataset. Therefore, there is no mention of dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data. Therefore, there are no training/test/validation dataset splits or cross-validation details mentioned. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe any empirical experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe any empirical experiments that would require specific software dependencies. Therefore, no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper describes theoretical algorithms and proofs, not empirical experiments. Therefore, there are no details provided about an experimental setup, hyperparameters, or system-level training settings. |