BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866911807832064000 |
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| author | Chakraborty, Arnab Raturi, Vikas Harsola, Shrutendra |
| author_facet | Chakraborty, Arnab Raturi, Vikas Harsola, Shrutendra |
| contents | We consider the problem of developing a clickstream modeling framework for real-time customer event prediction problems in SaaS products like QBO. We develop a low-latency, cost-effective, and robust ensemble architecture (BBE-LSWCM), which combines both aggregated user behavior data from a longer historical window (e.g., over the last few weeks) as well as user activities over a short window in recent-past (e.g., in the current session). As compared to other baseline approaches, we demonstrate the superior performance of the proposed method for two important real-time event prediction problems: subscription cancellation and intended task detection for QBO subscribers. Finally, we present details of the live deployment and results from online experiments in QBO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_16155 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models Chakraborty, Arnab Raturi, Vikas Harsola, Shrutendra Machine Learning We consider the problem of developing a clickstream modeling framework for real-time customer event prediction problems in SaaS products like QBO. We develop a low-latency, cost-effective, and robust ensemble architecture (BBE-LSWCM), which combines both aggregated user behavior data from a longer historical window (e.g., over the last few weeks) as well as user activities over a short window in recent-past (e.g., in the current session). As compared to other baseline approaches, we demonstrate the superior performance of the proposed method for two important real-time event prediction problems: subscription cancellation and intended task detection for QBO subscribers. Finally, we present details of the live deployment and results from online experiments in QBO. |
| title | BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2203.16155 |