Counterfactual Inference for Consumer Choice Across Many Product Categories

• URL: http://arxiv.org/abs/1906.02635v2
• Date: Sun, 6 Aug 2023 18:15:01 GMT
• Title: Counterfactual Inference for Consumer Choice Across Many Product Categories
• Authors: Rob Donnelly, Francisco R. Ruiz, David Blei, and Susan Athey
• Abstract summary: 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.
• Score: 6.347014958509367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different categories. Her preferences about product attributes as well as her price sensitivity vary across products and are in general correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes and products going out of stock. We evaluate the performance of the model using held-out data from weeks with price changes or out of stock products. We show that our model improves over traditional modeling approaches that consider each category in isolation. One source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts.

Related papers

• ComPO: Community Preferences for Language Model Personalization [122.54846260663922]
  ComPO is a method to personalize preference optimization in language models. We collect and release ComPRed, a question answering dataset with community-level preferences from Reddit.
  arXiv  Detail & Related papers  (2024-10-21T14:02:40Z)
• Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models [50.370043676415875]
  In smart retail applications, the large number of products and their frequent turnover necessitate reliable zero-shot object classification methods. We introduce the MIMEX dataset, comprising 28 distinct product categories. We benchmark the zero-shot object classification performance of state-of-the-art vision-language models (VLMs) on the proposed MIMEX dataset.
  arXiv  Detail & Related papers  (2024-09-23T12:28:40Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study [2.126171264016785]
  We develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online retailer in China, that is JD. Our results reveal that customers' product choice is insensitive to the promised delivery time, but this factor significantly impacts customers' order quantity. We identify product classes for which certain discounting approaches are more effective and provide recommendations on better use of different discounting tools.
  arXiv  Detail & Related papers  (2022-12-01T19:37:29Z)
• Learning Consumer Preferences from Bundle Sales Data [2.6899658723618005]
  We propose an approach to learn the distribution of consumers' valuations toward the products using bundle sales data. Using the EM algorithm and Monte Carlo simulation, our approach can recover the distribution of consumers' valuations.
  arXiv  Detail & Related papers  (2022-09-11T21:42:49Z)
• Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation [55.0391061198924]
  Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. It is nontrivial to incorporate price preferences for session-based recommendation. We propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
  arXiv  Detail & Related papers  (2022-05-09T10:47:15Z)
• Text Classification for Predicting Multi-level Product Categories [0.0]
  In an online shopping platform, a detailed classification of the products facilitates user navigation. In this study, we focus on product title classification of the grocery products.
  arXiv  Detail & Related papers  (2021-09-02T17:00:05Z)
• 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)
• Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
  We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features. We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
  arXiv  Detail & Related papers  (2020-12-21T01:01:56Z)
• Adversarial learning for product recommendation [0.0]
  This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers.
  arXiv  Detail & Related papers  (2020-07-07T23:35:36Z)
• Studying Product Competition Using Representation Learning [7.01269741110576]
  We introduce Product2Vec, a method based on the representation learning algorithm Word2Vec to study product-level competition. The proposed model takes shopping baskets as inputs and, for every product, generates a low-dimensional embedding that preserves important product information. We show that, compared with state-of-the-art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.
  arXiv  Detail & Related papers  (2020-05-21T00:36:13Z)

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.