Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations

• URL: http://arxiv.org/abs/2202.00146v1
• Date: Mon, 31 Jan 2022 23:26:17 GMT
• Title: Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
• Authors: Aleksey A. Kocherzhenko, Nirmal Sobha Kartha, Tengfei Li, Hsin-Yi (Jenny) Shih, Marco Mandic, Mike Fuller, Arshak Navruzyan
• Abstract summary: Personalization enables businesses to learn customer preferences from past interactions. We consider the problem of predicting the optimal promotional offer for a given customer out of several options as a contextual bandit problem.
• Score: 1.1213676742918772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalization enables businesses to learn customer preferences from past interactions and thus to target individual customers with more relevant content. We consider the problem of predicting the optimal promotional offer for a given customer out of several options as a contextual bandit problem. Identifying information for the customer and/or the campaign can be used to deduce unknown customer/campaign features that improve optimal offer prediction. Using a generated synthetic email promo dataset, we demonstrate similar prediction accuracies for (a) a wide and deep network that takes identifying information (or other categorical features) as input to the wide part and (b) a deep-only neural network that includes embeddings of categorical features in the input. Improvements in accuracy from including categorical features depends on the variability of the unknown numerical features for each category. We also show that selecting options using upper confidence bound or Thompson sampling, approximated via Monte Carlo dropout layers in the wide and deep models, slightly improves model performance.

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)
• Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning [48.94952630292219]
  We propose a novel client selection strategy designed to emulate the performance achieved with full client participation. In a single round, we select clients by minimizing the gradient-space estimation error between the client subset and the full client set. In multi-round selection, we introduce a novel individual fairness constraint, which ensures that clients with similar data distributions have similar frequencies of being selected.
  arXiv  Detail & Related papers  (2024-05-22T12:27:24Z)
• A Neural Network Based Choice Model for Assortment Optimization [5.173001988341294]
  We investigate whether a single neural network architecture can predict purchase probabilities for datasets from various contexts. Next, we develop an assortment optimization formulation that is solvable by off-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2023-08-10T15:01:52Z)
• Estimating Conditional Mutual Information for Dynamic Feature Selection [14.706269510726356]
  Dynamic feature selection is a promising paradigm to reduce feature acquisition costs and provide transparency into a model's predictions. Here, we take an information-theoretic perspective and prioritize features based on their mutual information with the response variable. Our method provides consistent gains over recent methods across a variety of datasets.
  arXiv  Detail & Related papers  (2023-06-05T23:03:03Z)
• Customer Churn Prediction Model using Explainable Machine Learning [0.0]
  Key objective of the paper is to develop a unique Customer churn prediction model which can help to predict potential customers who are most likely to churn. We evaluated and analyzed the performance of various tree-based machine learning approaches and algorithms. In order to improve Model explainability and transparency, paper proposed a novel approach to calculate Shapley values for possible combination of features.
  arXiv  Detail & Related papers  (2023-03-02T04:45:57Z)
• 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)
• Targeted Advertising on Social Networks Using Online Variational Tensor Regression [19.586412285513962]
  We propose what we believe is the first contextual bandit framework for online targeted advertising. The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor. We empirically confirm that the proposedUCB algorithm achieves a significant improvement in influence tasks over the benchmarks.
  arXiv  Detail & Related papers  (2022-08-22T22:10:45Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• 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)
• Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling [62.049330405736406]
  Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant. It is insufficient to provide the same "completely summarized" answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves. We propose a novel multi-perspective user preference model for generating personalized answers in PQA.
  arXiv  Detail & Related papers  (2021-12-27T07:51:49Z)
• 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)

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.