Robust portfolio optimization model for electronic coupon allocation

• URL: http://arxiv.org/abs/2405.12865v1
• Date: Tue, 21 May 2024 15:30:25 GMT
• Title: Robust portfolio optimization model for electronic coupon allocation
• Authors: Yuki Uehara, Naoki Nishimura, Yilin Li, Jie Yang, Deddy Jobson, Koya Ohashi, Takeshi Matsumoto, Noriyoshi Sukegawa, Yuichi Takano,
• Abstract summary: We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.
• Score: 6.729713185413412
• License:
• Abstract: 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.

Related papers

• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing [27.459774494479227]
  We present a unified user-item matching framework to simultaneously conduct item recommendation and user targeting with just one model. Our framework results in significant performance gains in comparison with the state-of-the-art methods, with greatly reduced cost on computing resources and daily maintenance.
  arXiv  Detail & Related papers  (2023-07-19T13:49:35Z)
• On Optimal Caching and Model Multiplexing for Large Model Inference [66.50550915522551]
  Large Language Models (LLMs) and other large foundation models have achieved noteworthy success, but their size exacerbates existing resource consumption and latency challenges. We study two approaches for mitigating these challenges: employing a cache to store previous queries and learning a model multiplexer to choose from an ensemble of models for query processing.
  arXiv  Detail & Related papers  (2023-06-03T05:01:51Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling [1.027974860479791]
  We study the Online Constrained Multiple-Choice Promotions Personalization Problem. Our work formalizes the problem as an Online Multiple Choice Knapsack Problem. We provide a real-time adaptive method that guarantees budget constraints compliance.
  arXiv  Detail & Related papers  (2021-08-11T15:09:16Z)
• PreSizE: Predicting Size in E-Commerce using Transformers [76.33790223551074]
  PreSizE is a novel deep learning framework which utilizes Transformers for accurate size prediction. We demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system.
  arXiv  Detail & Related papers  (2021-05-04T15:23:59Z)
• Offer Personalization using Temporal Convolution Network and Optimization [0.0]
  Increase in online shopping and high market competition has led to an increase in promotional expenditure for online retailers. We propose our approach to solve the offer optimization problem at the intersection of consumer, item and time in retail setting.
  arXiv  Detail & Related papers  (2020-10-14T10:59:34Z)
• TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning [36.38658213969406]
  We propose a novel user intent prediction model, TPG-DNN, to complete the challenging task. The proposed model has been widely used for the coupon allocation, advertisement and recommendation on Taobao platform.
  arXiv  Detail & Related papers  (2020-08-05T13:25:53Z)
• Bayesian Optimization for Selecting Efficient Machine Learning Models [53.202224677485525]
  We present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency. Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency.
  arXiv  Detail & Related papers  (2020-08-02T02:56:30Z)
• Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising [52.3825928886714]
  We formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
  arXiv  Detail & Related papers  (2020-06-29T18:50:35Z)
• Multi-Purchase Behavior: Modeling, Estimation and Optimization [0.9337154228221861]
  We present a parsimonious multi-purchase family of choice models called the Bundle-MVL-K family. We develop a binary search based iterative strategy that efficiently computes optimized recommendations for this model.
  arXiv  Detail & Related papers  (2020-06-14T23:47:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.