# Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform

arXiv: https://arxiv.org/abs/1408.6515 · HTML: https://mlsanity.com/paper/1408.6515/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2014.

## Built on by (most cited first, 6 of 6)

- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Revisiting Self-attention for Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2505.21811/) (2025)
- [Macro Graph of Experts for Billion-Scale Multi-Task Recommendation](https://mlsanity.com/paper/2506.10520/) (2025)
- [Bootstrapping your behavior: a new pretraining strategy for user behavior sequence data](https://mlsanity.com/paper/2506.11053/) (2025)
- [Action-Aware Generative Sequence Modeling for Short Video Recommendation](https://mlsanity.com/paper/2604.25834/) (2026)
- [SimTrace: Grounded Multimodal User Trajectories Generation for Online User Modeling](https://mlsanity.com/paper/2609.38397/) (2026)

## Abstract (arXiv)

This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014. With real-world user action data provided by Tmall, one of the largest B2C online retail platforms in China, this competition requires to predict future user purchases on Tmall website. Predictions are judged on F1Score, which considers both precision and recall for fair evaluation. The data set provided by Tmall contains more than half billion action records from over ten million distinct users. Such massive data volume poses a big challenge, and drives competitors to write every single program in MapReduce fashion and run it on distributed cluster. We model the purchase prediction problem as standard machine learning problem, and mainly employ regression and classification methods as single models. Individual models are then aggregated in a two-stage approach, using linear regression for blending, and finally a linear ensemble of blended models. The competition is approaching the end but still in running during writing this paper. In the end, our team achieves F1Score 6.11 and ranks 7th (out of 7,276 teams in total).