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

2024-07-18

arXiv.org

AI Summary

This paper addresses scalability, real-time responsiveness, and trustworthiness challenges hindering the industrial deployment of recommender systems in e-commerce, healthcare, and finance. It systematically surveys technical advances from 2017 to 2024, integrating classical paradigms—such as collaborative filtering and content-based filtering—with state-of-the-art approaches, including graph neural networks, reinforcement learning, and large language models. Methodologically, it introduces the first “theory–industrial practice” mapping framework and proposes a unified evaluation paradigm incorporating fairness, explainability, and cross-domain transferability. The contributions include a comprehensive taxonomy covering 12 recommendation paradigms across 8 major application domains, an industrial decision-making guide for algorithm selection, and the open-sourcing of multiple toolkits and benchmark datasets. These resources bridge academic research and industrial implementation, significantly advancing interdisciplinary collaboration and reproducible system development.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Cognitive Modeling & Cognitive Systems: Applications
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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 trends

Problem

• Bridging theory and practice in Recommender Systems
• Exploring advanced methods in personalized suggestions
• Addressing challenges in scalable, real-time solutions

Innovation

• Deep learning enhances recommender systems
• Graph-based models improve personalization
• Large language models drive real-time solutions