Predicting online shopping behaviour from clickstream data using deep learning

Koehn, Dennis; Lessmann, Stefan; Schaal, Markus

doi:10.1016/j.eswa.2020.113342

Cited by 118 publications

(79 citation statements)

References 34 publications

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“…Researchers in disparate domains find applications for CatBoost. We find works in the fields of Astronomy [ 18 ], Finance [ 19 – 22 ], Medicine [ 23 – 26 ], Biology [ 27 , 28 ], Electrical Utilities Fraud [ 29 – 31 ], Meteorology [ 32 , 33 ], Psychology [ 34 , 35 ], Traffic Engineering [ 7 , 36 ], Cyber-security [ 37 ], Bio-chemistry [ 5 , 38 ], and Marketing [ 39 ]. Therefore, a good understanding of CatBoost may provide one the opportunity to participate in interdisciplinary research.…”

Section: Introductionmentioning

confidence: 99%

CatBoost for big data: an interdisciplinary review

2020

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Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

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Section: Introductionmentioning

confidence: 99%

CatBoost for big data: an interdisciplinary review

2020

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“…Busy web sites with millions of daily users can generate large amounts of clickstream data that falls into the domain of Big Data. "Predicting online shopping behaviour from clickstream data using deep learning" by Koehn et al is a study on using ML to predict user behavior from clickstream data [50]. This study focuses largely on neural networks for making these predictions, but CatBoost plays a very important role.…”

Section: Marketingmentioning

confidence: 99%

“…Therefore, researchers considering taking on work to classify clickstream data may find that CatBoost will form part of an ensemble with the best performance. Figure 8 shows an image reproduced from Koehn et al [50,Fig.8], that demonstrates how the ensemble technique provides the best AUC for predicting user behavior from clickstream data. Another example of a study that shows the inferiority of gradient boosted tree algorithms to neural networks for ML tasks involving homogeneous data is "A clstmtmn for marketing intention detection" by Wang et al [83].…”

Section: Marketingmentioning

confidence: 99%

“…Researchers in disparate domains find applications for CatBoost. We find works in the fields of Astronomy [49], Finance [24] [92] [91] [89], Medicine [87] [52] [4] [68], Biology [51] [55], Electrical Utilities Fraud [20] [66] [36], Meteorology [44] [29], Psychology [71] [3], Traffic Engineering [85] [76], Cyber-security [6], Bio-chemistry [88] [58], and Marketing [50]. Therefore, a good understanding of CatBoost may provide one the opportunity to participate in interdisciplinary research.…”

Section: Introductionmentioning

confidence: 99%

“…Image from[50] illustrating best results for ensemble of CatBoost and GRU; Here, the authors refer to CatBoost as Gradient Boosted Machine (GBM)…”

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confidence: 99%

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CatBoost for Big Data: an Interdisciplinary Review

Hancock

Khoshgoftaar

2020

Preprint

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Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data, Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have ellCcessfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that .55 CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

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