Data Science and Management最新文献_第10页

Novel time slicing approach for customer defection models in e-commerce: a case study 电子商务中顾客流失模型的新时间切片方法:一个案例研究

Data Science and Management Pub Date : 2022-09-01 DOI: 10.1016/j.dsm.2022.07.001

Kyriakos Georgiou , Alexandros Chasapis

{"title":"Novel time slicing approach for customer defection models in e-commerce: a case study","authors":"Kyriakos Georgiou , Alexandros Chasapis","doi":"10.1016/j.dsm.2022.07.001","DOIUrl":"10.1016/j.dsm.2022.07.001","url":null,"abstract":"<div><p>In this study, we examine the problem of predicting customer defection in a noncontractual setting. Motivated by recent work on machine learning using multiple time slices, we develop a novel training and testing framework, the sliding multi-time slicing (SMTS) method. We apply this method to data from the largest marketplace in Greece, namely, Skroutz, considering the standard features that account for the important characteristics of customer activity and custom performance metrics aimed at capturing business-related goals established by the company. The dataset comprises customers over a relatively short period, since April 2018, the number of which has also exhibited a significant increase in recent months. Despite these difficulties and the inherent seasonality of customer defection, our results demonstrate that, with SMTS, developing models that outperform previous approaches and optimize decision-making is possible. We validate the approach to a benchmark dataset from the commerce sector and discuss the practical considerations and requirements of the proposed method.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000285/pdfft?md5=90cc770a3700d52be7c17ade53d2e0ae&pid=1-s2.0-S2666764922000285-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89955151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monitoring machine learning models: a categorization of challenges and methods 监测机器学习模型:挑战和方法的分类

Data Science and Management Pub Date : 2022-09-01 DOI: 10.1016/j.dsm.2022.07.004

Tim Schröder, Michael Schulz

引用次数: 10

Assessing spread risk of COVID-19 in early 2020 2020年初评估COVID-19的传播风险

Data Science and Management Pub Date : 2022-08-26 DOI: 10.1016/j.dsm.2022.08.004

S. Lai, I. Bogoch, N. Ruktanonchai, A. Watts, Xin Lu, Weizhong Yang, Hongjie Yu, K. Khan, A. Tatem

引用次数: 1

A hybrid differential evolution algorithm for a stochastic location-inventory-delivery problem with joint replenishment 联合补货随机定位-库存-交货问题的混合差分进化算法

Data Science and Management Pub Date : 2022-08-01 DOI: 10.1016/j.dsm.2022.07.003

Sirui Wang, Lin Wang, Yingying Pi

引用次数: 11

Forecasting hourly retail customer flow on intermittent time series with multiple seasonality 在具有多季节性的间歇时间序列上预测每小时零售客流

Data Science and Management Pub Date : 2022-07-01 DOI: 10.1016/j.dsm.2022.07.002

Martim Sousa, Ana Maria Tom, José Moreira

引用次数: 5

Effect of data resampling on feature importance in imbalanced blockchain data: comparison studies of resampling techniques 数据重采样对不平衡区块链数据特征重要性的影响:重采样技术的比较研究

Data Science and Management Pub Date : 2022-06-01 DOI: 10.1016/j.dsm.2022.04.003

Ismail Alarab, Simant Prakoonwit

{"title":"Effect of data resampling on feature importance in imbalanced blockchain data: comparison studies of resampling techniques","authors":"Ismail Alarab, Simant Prakoonwit","doi":"10.1016/j.dsm.2022.04.003","DOIUrl":"https://doi.org/10.1016/j.dsm.2022.04.003","url":null,"abstract":"<div><p>Cryptocurrency blockchain data encounter a class-imbalance problem due to only a few known labels of illicit or fraudulent activities in the blockchain network. For this purpose, we seek to compare various resampling methods applied to two highly imbalanced datasets derived from the blockchain of Bitcoin and Ethereum after further dimensionality reductions, which is different from previous studies on these datasets. Firstly, we study the performance of various classical supervised learning methods to classify illicit transactions or accounts on Bitcoin or Ethereum datasets, respectively. Consequently, we apply various resampling techniques to these datasets using the best performing learning algorithm on each of these datasets. Subsequently, we study the feature importance of the given models, wherein the resampled datasets directly influenced on the explainability of the model. Our main finding is that undersampling using the edited nearest-neighbour technique has attained an accuracy of more than 99% on the given datasets by removing the noisy data points from the whole dataset. Moreover, the best-performing learning algorithms have shown superior performance after feature reduction on these datasets in comparison to their original studies. The matchless contribution lies in discussing the effect of the data resampling on feature importance which is interconnected with explainable artificial intelligence (XAI) techniques.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000145/pdfft?md5=6bf238fdec6a4e856548c8bd4110e94a&pid=1-s2.0-S2666764922000145-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137349425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on value co-creation elements in full-scene intelligent service 全场景智能服务中价值协同创造要素研究

Data Science and Management Pub Date : 2022-06-01 DOI: 10.1016/j.dsm.2022.05.001

Weina Wang , Hong Zhang , Sumeet Gupta

引用次数: 7

New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight 利用人工智能和大数据预测风能的新进展:科学计量学的见解

Data Science and Management Pub Date : 2022-06-01 DOI: 10.1016/j.dsm.2022.05.002

Erlong Zhao , Shaolong Sun , Shouyang Wang

{"title":"New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight","authors":"Erlong Zhao , Shaolong Sun , Shouyang Wang","doi":"10.1016/j.dsm.2022.05.002","DOIUrl":"10.1016/j.dsm.2022.05.002","url":null,"abstract":"<div><p>Accurate forecasting results are crucial for increasing energy efficiency and lowering energy consumption in wind energy. Big data and artificial intelligence (AI) have great potential in wind energy forecasting. Although the literature on this subject is extensive, it lacks a comprehensive research status survey. In identifying the evolution rules of big data and AI methods in wind energy forecasting, this paper summarizes the studies on big data and AI in wind energy forecasting over the last two decades. The existing big data types, analysis techniques, and forecasting methods are classified and sorted by combining literature reviews and scientometrics methods. Furthermore, the research trend of wind energy forecasting methods is determined based on big data and artificial intelligence by combing the existing research hotspots and frontier progress. Finally, this paper summarizes existing research’s opportunities, challenges, and implications from various perspectives. The research results serve as a foundation for future research and promote the further development of wind energy forecasting.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000212/pdfft?md5=53efdc16a677b8948a6955a1f86304a5&pid=1-s2.0-S2666764922000212-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73276920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 45

A combined forecasting method for intermittent demand using the automotive aftermarket data 基于汽车售后市场数据的间歇性需求组合预测方法

Data Science and Management Pub Date : 2022-06-01 DOI: 10.1016/j.dsm.2022.04.001

Xiaotian Zhuang , Ying Yu , Aihui Chen

{"title":"A combined forecasting method for intermittent demand using the automotive aftermarket data","authors":"Xiaotian Zhuang , Ying Yu , Aihui Chen","doi":"10.1016/j.dsm.2022.04.001","DOIUrl":"10.1016/j.dsm.2022.04.001","url":null,"abstract":"<div><p>Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation, and accurate demand forecasting can reduce costs and increase efficiency for enterprises. This study proposes an intermittent demand combination forecasting method based on internal and external data, builds intermittent demand feature engineering from the perspective of machine learning, predicts the occurrence of demand by classification model, and predicts non-zero demand quantity by regression model. Based on the strategy selection on the inventory side and the stocking needs on the replenishment side, this study focuses on the optimization of the classification problem, incorporates the internal and external data of the enterprise, and proposes two combination forecasting optimization methods on the basis of the best classification threshold searching and transfer learning, respectively. Based on the real data of auto after-sales business, these methods are evaluated and validated in multiple dimensions. Compared with other intermittent forecasting methods, the models proposed in this study have been improved significantly in terms of classification accuracy and forecasting precision, which validates the potential of combined forecasting framework for intermittent demand and provides an empirical study of the framework in industry practice. The results show that this research can further provide accurate upstream inputs for smart inventory and guarantee intelligent supply chain decision-making in terms of accuracy and efficiency.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000121/pdfft?md5=b4e7fd469fe0882d34c5c1fd97ff6fc7&pid=1-s2.0-S2666764922000121-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80828936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Feature extraction of search product based on multi-feature fusion-oriented to Chinese online reviews 面向中文在线评论的基于多特征融合的搜索产品特征提取

Data Science and Management Pub Date : 2022-06-01 DOI: 10.1016/j.dsm.2022.04.002

Xunjiang Huang, Yaqian Liu, Yang Wang, Xue Wang

{"title":"Feature extraction of search product based on multi-feature fusion-oriented to Chinese online reviews","authors":"Xunjiang Huang, Yaqian Liu, Yang Wang, Xue Wang","doi":"10.1016/j.dsm.2022.04.002","DOIUrl":"10.1016/j.dsm.2022.04.002","url":null,"abstract":"<div><p>The increasing Chinese online reviews contain rich product demand information, especially for search products. This study suggests a product feature extraction model from online reviews based on multi-feature fusion named PFEMF (products features extraction based on multi-feature fusion) model. Combining sentence and word characteristics of Chinese online reviews, the model explores the lexical features, frequency features, span features, and semantic similarity features of words. And then, they are fused to identify the features that customers are concerned about most by sequential relationship analysis. The identified product feature provides direction for product innovation and facilitates the product selection for customers. Finally, the study takes iPad Air as an example to prove this model. The results show that the extraction performance of the PFEMF model is superior to the traditional term frequency-inverse document frequency (tf-idf) algorithm, word span algorithm, and semantic similarity algorithm.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000133/pdfft?md5=6b7a8eaaa58360079b9b945c9422c32c&pid=1-s2.0-S2666764922000133-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75872743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3