2021 4th International Conference on Artificial Intelligence for Industries (AI4I)最新文献

筛选

英文中文

When to Message: Investigating User Response Prediction with Machine Learning for Advertisement Emails 何时发送消息:用机器学习研究广告邮件的用户响应预测

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00014

Christian Bitter, Hasan Tercan, Tobias Meisen, Todd J. Bodnar, Philipp Meisen

{"title":"When to Message: Investigating User Response Prediction with Machine Learning for Advertisement Emails","authors":"Christian Bitter, Hasan Tercan, Tobias Meisen, Todd J. Bodnar, Philipp Meisen","doi":"10.1109/AI4I51902.2021.00014","DOIUrl":"https://doi.org/10.1109/AI4I51902.2021.00014","url":null,"abstract":"Direct marketing message campaigns are a common way for businesses to deliver updates, recommendations, or coupons to their user base to spark interest in brands and products. In this paper, we explore the possibility of using machine learning to predict the response behavior of users to regular newsletter emails from a real-world e-commerce business. In doing so, we train and evaluate classification models, such as random forests and artificial neural networks, to predict the probability of a user interacting with an email based on past behavior. Further investigation is conducted into the potential of using the sending time of a message to influence responses, based on the assumption that a user’s likeliness to be engaged depends on the time of day a message is received. We identify two user groups that have a preference regarding morning and evening messages and can show that this preference holds for a subsequent message campaign. Thus, our results demonstrate a clear potential for time-aware response modeling approaches for marketing campaigns.","PeriodicalId":114373,"journal":{"name":"2021 4th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127394611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developing Hierarchies for Image Classification Model Evaluation 图像分类模型评价的层次发展

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00016

Sami Wood, Erin Lanus, Daniel D. Doyle, Jeremy Ogorzalek, C. Franck, Laura J. Freeman

{"title":"Developing Hierarchies for Image Classification Model Evaluation","authors":"Sami Wood, Erin Lanus, Daniel D. Doyle, Jeremy Ogorzalek, C. Franck, Laura J. Freeman","doi":"10.1109/AI4I51902.2021.00016","DOIUrl":"https://doi.org/10.1109/AI4I51902.2021.00016","url":null,"abstract":"Classes within computer vision (CV) datasets often exhibit hierarchical structures such as super-subordinate IS-A relations. While some common performance metrics for evaluating CV models such as “top-5 error” ignore hierarchical structure, metrics for hierarchical scoring exist, yet effectiveness for meaningful evaluation is dependent on the ability of the hierarchy to reflect important semantic relationships between classes. Most hierarchical scoring methods reward closeness between prediction and ground truth classes. Such schemes may produce the same score when a child is misclassified as a terrorist as when a car is misclassified as a vehicle or helicopter, ignorant of the different levels of impact of these misclassifications.An approach for developing context-aware hierarchies for use with existing evaluation metrics to reflect the cost of misclassification is needed. The contribution of this paper is to provide a hierarchy construction framework that penalizes misclassifications accordingly given a list of importance ordered categories and a hierarchical scoring method. The framework is demonstrated in a hierarchy selection use case and compared quantitatively against the “top-5 error” metric and a simple super-subordinate relation hierarchical scoring. We qualitatively discuss the efficacy and implications of each approach.","PeriodicalId":114373,"journal":{"name":"2021 4th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125663866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Multi-point Indoor Temperature from Different Season Data based on Correlation-based Two-Step Learning 基于相关两步学习的不同季节多点室内温度估计

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00024

Keisuke Tsunoda, Midori Kodama, N. Arai, Souraro Maejima, Kazuaki Obana

{"title":"Estimating Multi-point Indoor Temperature from Different Season Data based on Correlation-based Two-Step Learning","authors":"Keisuke Tsunoda, Midori Kodama, N. Arai, Souraro Maejima, Kazuaki Obana","doi":"10.1109/AI4I51902.2021.00024","DOIUrl":"https://doi.org/10.1109/AI4I51902.2021.00024","url":null,"abstract":"This paper presents a method to estimate multi-point temperature in a large-scale indoor space on the basis of indoor temperature and Heating, Ventilation, and Air-Conditioning (HVAC) temperature measured for one day in the same season and data measured for a longer period in a different season. Existing studies have tried to learn an estimation or prediction model from such learning data on the basis of fine-tuning or transfer learning in which the loss function is calculated from differences such as mean squared error or accuracy between measured and estimated data. However, it is difficult for existing methods to estimate on the basis of data from a different season because the difference between indoor temperature and HVAC temperature depends on the season. In this paper, we focus on not the difference but the correlation between indoor temperature and HVAC temperature, which does not depend on seasons. We propose correlation-based two-step learning in which the loss function is calculated from the correlation between indoor temperature and HVAC temperature at the first learning. We evaluate the effectiveness of our proposal using measured indoor temperature and HVAC temperature data in a real building.","PeriodicalId":114373,"journal":{"name":"2021 4th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"28 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132089036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

首页上一页