Strategic Coupon Allocation for Increasing Providers' Sales Experiences in Two-sided Marketplaces

In a two-sided marketplace, network effects are crucial for competitiveness, and platforms need to retain users through advanced customer relationship management as much as possible. Maintaining numerous providers' stable and active presence on the platform is highly important to enhance the marketplace's scale and diversity. The strongest motivation for providers to continue using the platform is to realize actual profits through sales. Then, we propose a personalized promotion to increase the number of successful providers with sales experiences on the platform. The main contributions of our research are twofold. First, we introduce a new perspective in provider management with the distribution of successful sales experiences. Second, we propose a personalized promotion optimization method to maximize the number of providers' sales experiences. By utilizing this approach, we ensure equal opportunities for providers to experience sales without being monopolized by a few providers. Through experiments using actual data on coupon distribution, we confirm that our method enables the implementation of coupon allocation strategies that significantly increase the total number of providers having sales experiences.

Fast solution to the fair ranking problem using the Sinkhorn algorithm

In two-sided marketplaces such as online flea markets, recommender systems for providing consumers with personalized item rankings play a key role in promoting transactions between providers and consumers. Meanwhile, two-sided marketplaces face the problem of balancing consumer satisfaction and fairness among items to stimulate activity of item providers. Saito and Joachims (2022) devised an impact-based fair ranking method for maximizing the Nash social welfare based on fair division; however, this method, which requires solving a large-scale constrained nonlinear optimization problem, is very difficult to apply to practical-scale recommender systems. We thus propose a fast solution to the impact-based fair ranking problem. We first transform the fair ranking problem into an unconstrained optimization problem and then design a gradient ascent method that repeatedly executes the Sinkhorn algorithm. Experimental results demonstrate that our algorithm provides fair rankings of high quality and is about 1000 times faster than application of commercial optimization software.

Investigating the Potential of Using Large Language Models for Scheduling

The inaugural ACM International Conference on AI-powered Software introduced the AIware Challenge, prompting researchers to explore AI-driven tools for optimizing conference programs through constrained optimization. We investigate the use of Large Language Models (LLMs) for program scheduling, focusing on zero-shot learning and integer programming to measure paper similarity. Our study reveals that LLMs, even under zero-shot settings, create reasonably good first drafts of conference schedules. When clustering papers, using only titles as LLM inputs produces results closer to human categorization than using titles and abstracts with TFIDF. The code has been made publicly available.

Robust portfolio optimization model for electronic coupon allocation

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six types of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean-variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.

Generalized Regression with Conditional GANs

Regression is typically treated as a curve-fitting process where the goal is to fit a prediction function to data. With the help of conditional generative adversarial networks, we propose to solve this age-old problem in a different way; we aim to learn a prediction function whose outputs, when paired with the corresponding inputs, are indistinguishable from feature-label pairs in the training dataset. We show that this approach to regression makes fewer assumptions on the distribution of the data we are fitting to and, therefore, has better representation capabilities. We draw parallels with generalized linear models in statistics and show how our proposal serves as an extension of them to neural networks. We demonstrate the superiority of this new approach to standard regression with experiments on multiple synthetic and publicly available real-world datasets, finding encouraging results, especially with real-world heavy-tailed regression datasets. To make our work more reproducible, we release our source code. Link to repository: https://anonymous.4open.science/r/regressGAN-7B71/

Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions

Promotions have been trending in the e-commerce marketplace to build up customer relationships and guide customers towards the desired actions. Since incentives are effective to engage customers and customers have different preferences for different types of incentives, the demand for personalized promotion decision making is increasing over time. However, research on promotion decision making has focused specifically on purchase conversion during the promotion period (the direct effect), while generally disregarding the enduring effect in the post promotion period. To achieve a better lift return on investment (lift ROI) on the enduring effect of the promotion and improve customer retention and loyalty, we propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the predictions of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget. To estimate the effect of decision making, we apply an unbiased evaluation approach of business metrics with randomized control trial (RCT) data. We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results.