Leverage LLM for Next-Gen Recommender Systems: The Evolution of Recommender Systems and Rise of LLMs

Author: Nishant Satya Lakshmikanth, Engineering Leader, LinkedIn Corporation

Introduction

Recommender systems have come a long way—from basic rule-based approaches to sophisticated machine learning pipelines that serve billions of personalized suggestions daily. This part sets the stage by tracing that evolution and introducing how large language models (LLMs) are transforming the landscape. As part of the broader open-source efforts under LF AI & Data’s GenAI Commons, this exploration connects research in recommender systems with community-driven initiatives that aim to make GenAI more accessible, interoperable, and impactful. We’ll begin with the historical context and move into the early integration patterns of LLMs, setting up the groundwork for more advanced applications explored in later parts.

Context

This article explores the evolution of recommender systems and how Large Language Models (LLMs) are shaping their next generation. It reviews foundational techniques such as collaborative and content-based filtering and highlights recent advances including deep learning, graph neural networks, and transformers. The integration of LLMs is categorized into multiple forms—embedding-based, generative, and hybrid—each enhancing recommendation quality through improved context understanding, personalization, and explainability. The paper discusses how LLMs can support user interaction, domain adaptation, and evaluation across uni-modal and multi-modal data settings. It also presents emerging techniques like instruction tuning, prompt tuning, LoRA, and in-context learning to optimize LLMs without full retraining.

In alignment with LF AI & Data’s GenAI Commons initiative, this work emphasizes open, community-driven approaches to these advancements. By connecting recommender system research with shared GenAI practices, the article highlights not only technical progress but also how collaborative frameworks can help address key challenges such as scalability, efficiency, fairness, privacy, and dynamic user preferences. Overall, the work advocates for transitioning from LLM-enhanced pipelines to fully generative, interpretable, and adaptive recommendation systems that meet real-world demands.

Introduction to Recommender Systems

Recommender systems are essential for helping users discover relevant products, content, and services. They have evolved from simple rule-based models to advanced machine learning-driven approaches that analyze large-scale data.

Early systems relied on collaborative filtering and content-based filtering. Collaborative filtering predicts user preferences by analyzing patterns among similar users, while content-based filtering recommends items based on shared attributes. Large-scale implementations often use Singular Value Decomposition (SVD) and deep-learning-based embeddings to improve accuracy and handle sparse data. Recent advancements, including deep learning, Graph Neural Networks (GNNs) [2], and transformers, have significantly enhanced recommendation quality. Deep-learning embeddings, as seen in YouTube, model long-term engagement, while GNNs, used in LinkedIn’s People You May Know, analyze social graphs for better connections. Transformers, such as those powering Spotify’s recommendations, capture sequential user behavior for precise personalization. Hybrid models, real-time processing, and reinforcement learning have further improved adaptability, making recommendations more dynamic and responsive.

With the rise of Large Language Models (LLMs), recommendation systems are becoming more contextual, conversational, and personalized. Unlike traditional filtering methods, LLMs bring deeper context awareness and human-like text generation, allowing for more intuitive and adaptable recommendations. As AI continues to evolve, the future of recommender systems will focus on explainability, fairness, and efficiency, ensuring high-quality recommendations while addressing bias and scalability challenges.

Leveraging LLMs in Different Forms

The following summarizes the different forms of leveraging LLMs in various forms.

LLMs in Recommendation System Types	Details
Recommender AI Agent	LLMs enable natural interactions and intelligent responses but often lack deep domain expertise. Traditional recommender systems, on the other hand, excel with in-domain data but are limited to structured inputs and lack interactivity. InteRecAgent bridges this gap by integrating LLMs as the reasoning engine and traditional recommender models as tools. This approach transforms models like matrix factorization into interactive, conversational, and explainable recommender systems. Ref: [1, 2]
Selective Knowledge Plugin	Enhancing an LLM’s domain-specific ability without fine-tuning requires strategic prompt engineering. In this work, the authors propose a method that augments LLMs with selective knowledge, allowing them to incorporate large-scale, evolving, and domain-specific data patterns dynamically. By structuring prompts effectively, the authors enable the model to reason with relevant information, making it adaptable to specialized tasks without modifying its core parameters. Ref: [1]
Embedding LLMs	Dense retrieval is critical in applications such as recommender systems and retrieval-augmented generation (RAG), where identifying relevant items quickly and accurately is essential. While generative language models excel at producing coherent text, they are not inherently optimized for creating high-quality embeddings used in retrieval tasks. To bridge this gap, specialized embedding models—often fine-tuned for item retrieval—are used to represent various text-based inputs, such as search queries, product descriptions, and user instructions, in a way that supports efficient and accurate retrieval from large-scale vector databases. Ref: [1, 2]
Model Explainer	Deep learning-based recommender systems are widely used in online services for their accuracy and scalability. However, their complex architectures often make them difficult to interpret, reducing transparency for both users and developers. To improve trust and understanding, large language models (LLMs) can be employed as surrogate models that learn to replicate and explain the behavior of these recommendation systems. This approach enables interpretability without significantly impacting recommendation quality. Ref: [1, 2]
Recommender LLM Evaluator	Evaluation is essential for understanding a model’s true capabilities and pinpointing areas for improvement. As language models increasingly serve as recommenders, their evaluation methods have diverged from traditional approaches. This project introduces a comprehensive evaluation service for LM-based recommender systems. Whether assessing a trained model or an API (e.g., Azure OpenAI API), it evaluates key aspects such as retrieval effectiveness, ranking accuracy, explanation quality, and general AI capabilities. Ref: [1, 2, 3]

Table-1: Classification of LLMs (source)

Bridging to What Comes Next

While this overview introduces the different ways LLMs are being utilized in recommender systems, the next step is to understand how these ideas come together in a full-stack architecture. Part 2 will explore the technical building blocks that enable LLMs to function as recommendation engines—including embedding pipelines, multi-modal representations, and fine-tuning strategies like instruction tuning and LoRA. Then in Part 3, we’ll unpack the practical challenges of deploying these systems at scale—from compute costs and inference latency to fairness and privacy concerns. Framing these discussions within LF AI & Data’s GenAI Commons shows how community-driven efforts can make such systems more sustainable and production-ready.

Conclusion

In this first part, we established the evolution of recommender systems and examined how LLMs are broadening the landscape through multiple roles—from reasoning engines and plug-ins to evaluators and explainers. These functional shifts mark the beginning of a paradigm change where LLMs are not just enhancements but strategic building blocks in personalized recommendation. In line with LF AI & Data’s GenAI Commons, this journey also reflects a broader community effort to shape open, interoperable GenAI practices. In the next part, we will go deeper into the technical frameworks, models, and optimization strategies that bring this transformation to life.

Acknowledgment

Special thanks to Ofer Hermoni, Sandeep Jha, and the members of LF AI & Data’s GenAI Commons for their input and collaboration. The group’s open discussions, shared frameworks, and cross-community efforts continue to shape how generative AI—and recommender systems in particular—are advanced in practice.

Reference

Author

LF AI & Data

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