Product Ranking for Revenue Maximization with Multiple Purchases

• URL: http://arxiv.org/abs/2210.08268v1
• Date: Sat, 15 Oct 2022 11:59:45 GMT
• Title: Product Ranking for Revenue Maximization with Multiple Purchases
• Authors: Renzhe Xu, Xingxuan Zhang, Bo Li, Yafeng Zhang, Xiaolong Chen, Peng Cui
• Abstract summary: We propose an optimal ranking policy when the online retailer can precisely model consumers' behaviors. We develop the Multiple-Purchase-with-Budget UCB algorithms with $O(sqrtT)$ regret. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.
• Score: 29.15026863056805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively. Under this setting, we first design an optimal ranking policy when the online retailer can precisely model consumers' behaviors. Based on the policy, we further develop the Multiple-Purchase-with-Budget UCB (MPB-UCB) algorithms with $\~O(\sqrt{T})$ regret that estimate consumers' behaviors and maximize revenue simultaneously in online settings. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.

Related papers

• Procurement Auctions via Approximately Optimal Submodular Optimization [53.93943270902349]
  We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs. Our goal is to design computationally efficient auctions that maximize the difference between the quality of the acquired services and the total cost of the sellers.
  arXiv  Detail & Related papers  (2024-11-20T18:06:55Z)
• 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)
• Unlocking the `Why' of Buying: Introducing a New Dataset and Benchmark for Purchase Reason and Post-Purchase Experience [24.949929747493204]
  We propose purchase reason prediction as a novel task for modern AI models. We first generate a dataset that consists of real-world explanations of why users make certain purchase decisions for various products. Our approach induces LLMs to explicitly distinguish between the reasons behind purchasing a product and the experience after the purchase in a user review.
  arXiv  Detail & Related papers  (2024-02-20T23:04:06Z)
• Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
  A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times. This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
  arXiv  Detail & Related papers  (2022-02-26T02:07:25Z)
• 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)
• User-Inspired Posterior Network for Recommendation Reason Generation [53.035224183349385]
  Recommendation reason generation plays a vital role in attracting customers' attention as well as improving user experience. We propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests. Experimental results show that our model is superior to traditional generative models.
  arXiv  Detail & Related papers  (2021-02-16T02:08:52Z)
• Revenue Maximization and Learning in Products Ranking [1.7403133838762448]
  We consider the revenue problem for an online retailer who plans to display a set of products differing in their prices and qualities and rank them in order order. The consumers have random attention spans and view the products sequentially purchasing a satificsing'' product or leaving the platform empty-handed when the attention span gets exhausted. Our framework extends the cascade model in two directions: the consumers have random attention spans instead of fixed ones and the firm maximizes revenues instead of clicking probabilities.
  arXiv  Detail & Related papers  (2020-12-07T15:41:57Z)
• Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural Network [0.0]
  Accurate prediction of consumer purchase pattern enables better inventory planning and efficient personalized marketing strategies. Nerve network architectures like Multi Layer Perceptron, Long Short Term Memory (LSTM), Temporal Convolutional Networks (TCN) and TCN-LSTM bring over ML models like Xgboost and RandomForest.
  arXiv  Detail & Related papers  (2020-10-14T11:00:00Z)
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
  We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
  arXiv  Detail & Related papers  (2020-07-14T06:06:41Z)
• Assortment Optimization with Repeated Exposures and Product-dependent Patience Cost [19.29174615532181]
  We study the assortment optimization problem faced by many online retailers such as Amazon. We develop a multinomial logit model to capture the consumers' purchasing behavior across multiple stages.
  arXiv  Detail & Related papers  (2020-02-13T03:12:49Z)
• Counterfactual Inference for Consumer Choice Across Many Product Categories [6.347014958509367]
  We build on techniques from the machine learning literature on probabilistic models of matrix factorization. We show that our model improves over traditional modeling approaches that consider each category in isolation. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product.
  arXiv  Detail & Related papers  (2019-06-06T15:11:01Z)

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.