# A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice

arXiv: https://arxiv.org/abs/2407.13699 · HTML: https://mlsanity.com/paper/2407.13699/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2024.

## Built on by (most cited first, 14 of 14)

- [A Primer on Quantum Machine Learning](https://mlsanity.com/paper/2511.15969/) (2025)
- [Dynamic Forgetting and Spatio-Temporal Periodic Interest Modeling for Local-Life Service Recommendation](https://mlsanity.com/paper/2508.02451/) (2025)
- [Retentive Relevance: Capturing Long-Term User Value in Recommendation Systems](https://mlsanity.com/paper/2510.07621/) (2025)
- [Iconographic Classification and Content-Based Recommendation for Digitized Artworks](https://mlsanity.com/paper/2602.19698/) (2026)
- [Towards Reliable Recommender Systems for Rating Data](https://mlsanity.com/paper/2412.20802/) (2024)
- [Global dense vector representations for words or items using shared parameter alternating Tweedie model](https://mlsanity.com/paper/2501.00623/) (2024)
- [Introducing the Swiss Food Knowledge Graph: AI for Context-Aware Nutrition Recommendation](https://mlsanity.com/paper/2507.10156/) (2025)
- [Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation](https://mlsanity.com/paper/2508.12674/) (2025)
- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)
- [PromptHelper: A Prompt Recommender System for Encouraging Creativity in AI Chatbot Interactions](https://mlsanity.com/paper/2601.15575/) (2026)
- [AlphaFree: Recommendation Free from Users, IDs, and GNNs](https://mlsanity.com/paper/2603.02653/) (2026)
- [APeB: Benchmarking Personalization Ability of Large Language Model Agents](https://mlsanity.com/paper/2607.03162/) (2026)
- [RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2608.30384/) (2026)
- [MARS: Multi-resolution Adaptive Routing for Sequential Recommendation](https://mlsanity.com/paper/2610.07505/) (2026)

## Abstract (arXiv)

Recommender Systems (RS) play an integral role in enhancing user experiences by providing personalized item suggestions. This survey reviews the progress in RS inclusively from 2017 to 2024, effectively connecting theoretical advances with practical applications. We explore the development from traditional RS techniques like content-based and collaborative filtering to advanced methods involving deep learning, graph-based models, reinforcement learning, and large language models. We also discuss specialized systems such as context-aware, review-based, and fairness-aware RS. The primary goal of this survey is to bridge theory with practice. It addresses challenges across various sectors, including e-commerce, healthcare, and finance, emphasizing the need for scalable, real-time, and trustworthy solutions. Through this survey, we promote stronger partnerships between academic research and industry practices. The insights offered by this survey aim to guide industry professionals in optimizing RS deployment and to inspire future research directions, especially in addressing emerging technological and societal trendsfootnote. The survey resources are available in the public GitHub repository https://github.com/VectorInstitute/Recommender-Systems-Survey. (Recommender systems, large language models, chatgpt, responsible AI)