# How LLMs Are Transforming Recommendation Systems

LinkedIn is reshaping its news feed with AI technology.

- **Source:** [Podcast Archives - Software Engineering Daily](https://brifing.si/source/ebac6c96-a262-4d7d-b25b-c51b890e7d05)
- **Published:** 2026-08-18
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://softwareengineeringdaily.com/podcasts/how-llms-are-reshaping-recommendation-systems/>
- **On Brifing:** https://brifing.si/s/62bccf77-d850-4bb8-b01e-dcd42a2c3ed8

## Traditional Recommendation Systems

Recommendation systems have long relied on deep learning architectures. These systems scored each candidate item independently to capture user interest. However, this approach lacked the ability to process content like language, limiting user experience.

## The Rise of LLMs

Large Language Models (LLMs) have revolutionized recommendation systems. This technology enables content to be processed like language. LinkedIn rebuilt its news feed using LLMs, predicting user needs similarly to predicting the next word in a sentence.

## LinkedIn's Innovative Approach

LinkedIn redesigned its news feed by treating content recommendation as a sequence modeling problem. This approach combines the power of LLMs with traditional signals. Tim Jurka, a VP of Engineering at LinkedIn, led this transformative process.

## Cost and Scale Challenges

Using LLMs introduces new engineering challenges, such as cost and scale. LinkedIn developed innovative solutions to manage inference costs at massive scale. This ensures the system remains both efficient and cost-effective.

## Managing Content Quality

LinkedIn uses natural language policies to steer content quality. This ensures users encounter more relevant and high-quality content. LLMs play a critical role in this process, enhancing the overall user experience.

## Improvements in User Experience

LLM-based recommendation systems significantly enhance user experience. Users encounter more personalized and engaging content, increasing platform loyalty. This transformation underscores the power of LLMs in digital platforms.

## Future Potential

The role of LLMs in recommendation systems is expected to grow further. This technology could drive similar transformations across various industries. LinkedIn's success highlights the potential in this field.

## Conclusion and Significance

LLMs are fundamentally transforming recommendation systems. LinkedIn's innovations mark a significant step in both technology and user experience. These developments will shape the future of digital platforms.

## Sources covering this story

- https://softwareengineeringdaily.com/podcasts/how-llms-are-reshaping-recommendation-systems/ — 

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_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._