# EdgeRec: Recommender System on Edge in Mobile Taobao

arXiv: https://arxiv.org/abs/2005.08416 · HTML: https://mlsanity.com/paper/2005.08416/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 29 of 29)

- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [Device-Cloud Collaborative Learning for Recommendation](https://mlsanity.com/paper/2104.06624/) (2021)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction](https://mlsanity.com/paper/2104.09713/) (2021)
- [MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation](https://mlsanity.com/paper/2407.01972/) (2024)
- [Intelligent Request Strategy Design in Recommender System](https://mlsanity.com/paper/2206.12296/) (2022)
- [Device-Cloud Collaborative Recommendation via Meta Controller](https://mlsanity.com/paper/2207.03066/) (2022)
- [Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework](https://mlsanity.com/paper/2507.04636/) (2025)
- [On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems](https://mlsanity.com/paper/2201.10382/) (2022)
- [MC2-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation](https://mlsanity.com/paper/2109.12314/) (2021)
- [GPU-based Private Information Retrieval for On-Device Machine Learning Inference](https://mlsanity.com/paper/2301.10904/) (2023)
- [Edge-cloud Collaborative Learning with Federated and Centralized Features](https://mlsanity.com/paper/2304.05871/) (2023)
- [DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation](https://mlsanity.com/paper/2406.08804/) (2024)
- [Forward Once for All: Structural Parameterized Adaptation for Efficient Cloud-coordinated On-device Recommendation](https://mlsanity.com/paper/2501.02837/) (2025)
- [On-Device Model Fine-Tuning with Label Correction in Recommender Systems](https://mlsanity.com/paper/2211.01163/) (2022)
- [A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search](https://mlsanity.com/paper/2405.15521/) (2024)
- [Hgformer: Hyperbolic Graph Transformer for Recommendation](https://mlsanity.com/paper/2502.15693/) (2024)
- [Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions](https://mlsanity.com/paper/2504.15300/) (2025)
- [Mobile Supply: The Last Piece of Jigsaw of Recommender System](https://mlsanity.com/paper/2308.03855/) (2023)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (2023)
- [Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System](https://mlsanity.com/paper/2406.17289/) (2024)
- [Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering](https://mlsanity.com/paper/2606.21540/) (2026)
- [Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation](https://mlsanity.com/paper/2202.10236/) (2022)
- [ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence](https://mlsanity.com/paper/2203.13061/) (2022)
- [Device Tuning for Multi-Task Large Model](https://mlsanity.com/paper/2302.10820/) (2023)
- [COUPA: An Industrial Recommender System for Online to Offline Service Platforms](https://mlsanity.com/paper/2304.12549/) (2023)
- [Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation](https://mlsanity.com/paper/2407.18937/) (2024)
- [CHORD: Customizing Hybrid-precision On-device Model for Sequential Recommendation with Device-cloud Collaboration](https://mlsanity.com/paper/2510.03038/) (2025)
- [MALLOC: Benchmarking the Memory-aware Long Sequence Compression for Large Sequential Recommendation](https://mlsanity.com/paper/2601.20234/) (2026)

## Abstract (arXiv)

Recommender system (RS) has become a crucial module in most web-scale applications. Recently, most RSs are in the waterfall form based on the cloud-to-edge framework, where recommended results are transmitted to edge (e.g., user mobile) by computing in advance in the cloud server. Despite effectiveness, network bandwidth and latency between cloud server and edge may cause the delay for system feedback and user perception. Hence, real-time computing on edge could help capture user preferences more preciously and thus make more satisfactory recommendations. Our work, to our best knowledge, is the first attempt to design and implement the novel Recommender System on Edge (EdgeRec), which achieves Real-time User Perception and Real-time System Feedback. Moreover, we propose Heterogeneous User Behavior Sequence Modeling and Context-aware Reranking with Behavior Attention Networks to capture user's diverse interests and adjust recommendation results accordingly. Experimental results on both the offline evaluation and online performance in Taobao home-page feeds demonstrate the effectiveness of EdgeRec.