# BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models

arXiv: https://arxiv.org/abs/2203.16155 · HTML: https://mlsanity.com/paper/2203.16155/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 15 of 15)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems](https://mlsanity.com/paper/1904.04381/) (2019)
- [I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application](https://mlsanity.com/paper/1910.01447/) (2019)
- [Customer churn prediction in telecom using machine learning and social network analysis in big data platform](https://mlsanity.com/paper/1904.00690/) (2019)
- [A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data](https://mlsanity.com/paper/1612.06589/) (2016)
- [Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model](https://mlsanity.com/paper/1710.02262/) (2017)
- [Predicting online user behaviour using deep learning algorithms](https://mlsanity.com/paper/1511.06247/) (2015)

## Abstract (arXiv)

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.