# A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives

arXiv: https://arxiv.org/abs/2509.06002 · HTML: https://mlsanity.com/paper/2509.06002/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2025.

## Built on by (most cited first, 7 of 7)

- [Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback](https://mlsanity.com/paper/2602.12612/) (2026)
- [MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation](https://mlsanity.com/paper/2510.15286/) (2025)
- [TRU: Targeted Reverse Update for Efficient Multimodal Recommendation Unlearning](https://mlsanity.com/paper/2604.02183/) (2026)
- [The Unreasonable Effectiveness of Data for Recommender Systems](https://mlsanity.com/paper/2604.06420/) (2026)
- [TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation](https://mlsanity.com/paper/2605.13433/) (2026)
- [DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks](https://mlsanity.com/paper/2608.11873/) (2026)
- [Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy](https://mlsanity.com/paper/2608.13990/) (2026)

## Abstract (arXiv)

Recommender systems have generated tremendous value for both users and businesses, drawing significant attention from academia and industry alike. However, due to practical constraints, academic research remains largely confined to offline dataset optimizations, lacking access to real user data and large-scale recommendation platforms. This limitation reduces practical relevance, slows technological progress, and hampers a full understanding of the key challenges in recommender systems. In this survey, we provide a systematic review of industrial recommender systems and contrast them with their academic counterparts. We highlight key differences in data scale, real-time requirements, and evaluation methodologies, and we summarize major real-world recommendation scenarios along with their associated challenges. We then examine how industry practitioners address these challenges in Transaction-Oriented Recommender Systems and Content-Oriented Recommender Systems, a new classification grounded in item characteristics and recommendation objectives. Finally, we outline promising research directions, including the often-overlooked role of user decision-making, the integration of economic and psychological theories, and concrete suggestions for advancing academic research. Our goal is to enhance academia's understanding of practical recommender systems, bridge the growing development gap, and foster stronger collaboration between industry and academia.