LinkedIn Algo Changes (and What They Mean for Your Feed)

LinkedIn is a platform known for less fluff, providing meaningful and relevant content to enhance professional growth and networking.

Continuing to stay relevant as the platform grows means continuous improvement in how the platform delivers content based on each member's unique goals and preferences.

The tech team over there recently released info on efforts to modernize architecture and improve performance.

They're claiming the enhancements will improve feed content and also hold promise for broader applications across LinkedIn's large-scale modeling endeavors.

In this blog post, we'll summarize the company's announcement about the new tech advances, including a model to handle a larger number of parameters that they claim results in a notable upgrade in content quality.

By harnessing the power of deep learning and large datasets, they're supposedly poised to deliver even more personalized feed content to members.

Transforming Sparse ID Features

The Homepage Feed on LinkedIn generates billions of datasets daily over millions of sparse IDs.

To enhance the performance and personalization of the feed, they've incorporated the representation of sparse IDs as features into their recommendation algorithms.

By transforming these features into embeddings, using lookup tables, they say they can capture essential relationships within the data and significantly enhance computational efficiency.

Enriching Member Interaction History

Incorporating member interaction history into the search model has been pivotal for platforms like this.

Notably, LinkedIn aggregates embeddings of members that a particular user has interacted with.

This comprehensive representation, combined with individual ID embeddings, provides a holistic view of member preferences, enriching content recommendations.

Of course, for wild, spaghetti-wall style networking attempts, this could totally distort someone's feed. So, word to the wise and all that.

Expanding with Dense Gating and Larger MLP Layers

LinkedIn's renewed focus on personalized embeddings has brought focus to dense features. They will now be incorporating gating mechanisms to hidden layers, which should streamline information flow and improve the learning process, resulting in more accurate predictions.

(Does this sound like predictive intelligence to anyone else?)

Serving Scalability and Performance Enhancement

Transitioning from external serving to in-memory serving is a new effort that they say will significantly improve feature delivery and model fidelity. This strategic shift has provided more iteration flexibility and feature fidelity, offering a smoother user experience.

Collision-Free Hashing for Memory Efficiency

By adopting minimal perfect hashing, the company is striving to reduce memory usage for model artifacts. This approach, while introducing a slight latency trade-off, has proven to be highly efficient in managing large-scale models.

Empowered by the LinkedIn Algo?

As LinkedIn scales its model sizes and explores new territories, time will tell if things like GPU serving, intelligent model routing, and advanced feature embedding strategies shift the user experience or the strategies marketers need to win on the platform.