# BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service

arXiv: https://arxiv.org/abs/2211.12033 · HTML: https://mlsanity.com/paper/2211.12033/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction](https://mlsanity.com/paper/2006.05639/) (2020)
- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://mlsanity.com/paper/2105.06067/) (2021)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [Unbiased Learning to Rank with Unbiased Propensity Estimation](https://mlsanity.com/paper/1804.05938/) (2018)
- [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://mlsanity.com/paper/2108.04468/) (2021)
- [Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation](https://mlsanity.com/paper/1601.02828/) (2016)
- [Personalized Transfer of User Preferences for Cross-domain Recommendation](https://mlsanity.com/paper/2110.11154/) (2021)
- [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://mlsanity.com/paper/2205.10249/) (2022)
- [Adaptive Domain Interest Network for Multi-domain Recommendation](https://mlsanity.com/paper/2206.09672/) (2022)
- [Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling](https://mlsanity.com/paper/2201.06814/) (2022)
- [Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback](https://mlsanity.com/paper/2003.01892/) (2020)
- [APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2203.16218/) (2022)
- [Automatic Expert Selection for Multi-Scenario and Multi-Task Search](https://mlsanity.com/paper/2205.14321/) (2022)
- [ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation](https://mlsanity.com/paper/2104.02262/) (2021)
- [Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services](https://mlsanity.com/paper/2209.09427/) (2022)
- [DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network](https://mlsanity.com/paper/2011.11938/) (2020)
- [Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences](https://mlsanity.com/paper/2208.04022/) (2022)

## Built on by (most cited first, 5 of 5)

- [ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint](https://mlsanity.com/paper/2307.09193/) (2023)
- [Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction](https://mlsanity.com/paper/2308.15703/) (2023)
- [CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services](https://mlsanity.com/paper/2308.08446/) (2023)
- [Mobile Supply: The Last Piece of Jigsaw of Recommender System](https://mlsanity.com/paper/2308.03855/) (2023)
- [Spatial-Temporal Knowledge Distillation for Takeaway Recommendation](https://mlsanity.com/paper/2412.16502/) (2024)

## Abstract (arXiv)

Online Food Ordering Service (OFOS) is a popular location-based service that helps people to order what you want. Compared with traditional e-commerce recommendation systems, users' interests may be diverse under different spatiotemporal contexts, leading to various spatiotemporal data distribution, which limits the fitting capacity of the model. However, numerous current works simply mix all samples to train a set of model parameters, which makes it difficult to capture the diversity in different spatiotemporal contexts. Therefore, we address this challenge by proposing a Bottom-up Adaptive Spatiotemporal Model(BASM) to adaptively fit the spatiotemporal data distribution, which further improve the fitting capability of the model. Specifically, a spatiotemporal-aware embedding layer performs weight adaptation on field granularity in feature embedding, to achieve the purpose of dynamically perceiving spatiotemporal contexts. Meanwhile, we propose a spatiotemporal semantic transformation layer to explicitly convert the concatenated input of the raw semantic to spatiotemporal semantic, which can further enhance the semantic representation under different spatiotemporal contexts. Furthermore, we introduce a novel spatiotemporal adaptive bias tower to capture diverse spatiotemporal bias, reducing the difficulty to model spatiotemporal distinction. To further verify the effectiveness of BASM, we also novelly propose two new metrics, Time-period-wise AUC (TAUC) and City-wise AUC (CAUC). Extensive offline evaluations on public and industrial datasets are conducted to demonstrate the effectiveness of our proposed modle. The online A/B experiment also further illustrates the practicability of the model online service. This proposed method has now been implemented on the Ele.me, a major online food ordering platform in China, serving more than 100 million online users.