Two people sit next to each other on a sofa in a living room. One person uses a TV remote to navigate a streaming service selection menu displayed on a flat-screen TV

Recommendation Systems: Using AI to Enhance Relevance and Value

Drive high user engagement and sales with recommendation (or recommender) systems that use powerful AI hardware and software solutions to match the right content to the right users.

Recommendation System Takeaways

  • Recommendation systems use AI to suggest relevant and desired content, products, and services to end users.

  • A good recommendation system will be accurate and cost-efficient.

  • Recommenders use a combination of several AI models to perform classification, recall, and ranking.

  • Workload-intensive recommender systems scale AI training and inference through parallelization on data center infrastructure.

  • Leading recommenders leverage a combination of Intel® GPUs, AI processors, high-core-count processors, and optimized software.



Recommendation Systems Keep Users Engaged

Recommendation systems or recommender systems are AI workflows that suggest products, content, or services to end users. The most common type of AI recommender systems are content recommenders used by streaming and e-commerce platforms.

These systems use a combination of technologies such as collaborative filtering to estimate a specific end user’s affinity for a movie or television series they might want to watch or a new product they might want to buy. Recommender systems keep end users engaged with the platform so they continue subscribing, buying products, or viewing ads while they consume content.

What Makes a Good Recommender System?

The effectiveness of recommendation systems depends largely on three factors: accuracy, responsiveness, and cost.

  • Accuracy refers to making recommendations that are sufficiently personalized to the end user’s interests and tastes. Recommenders can improve their accuracy by integrating a feedback mechanism, such as a simple thumbs up or thumbs down, that allows end users to indicate whether a recommendation is good or bad, reinforcing the AI’s learning.
  • Responsiveness refers to the recommender’s ability to present new options quickly and keep users engaged. Long load times or any measure of difficulty in engaging with a platform is known as friction, and more friction results in more users logging out or leaving the platform.
  • Cost refers to the initial investments or CapEx of the recommendation system, balanced against ongoing operational expenditures or OpEx. Cost efficiency also considers scaling workloads to meet user demand, minimizing downtime, and managing workloads across cloud environments.

Who Uses Recommendation Systems?

Most recommendation systems are consumer facing and are prominent in e-commerce, which recommends products for online shoppers; social media, which recommends content and creators and orders items within a feed or timeline; and streaming entertainment, which recommends engaging content to keep users subscribed to the platform. Personalized banking is an example of an emerging use case in which banks recommend new types of accounts, investments, and other services to grow their customer bases.

How Recommendation Systems Work

Recommender models are a pipeline of several different AI models and data analytics workflows. This pipeline can include up to a hundred or more different processes within a chain to deliver a unified prediction on a user-by-user basis. In any given recommender workflow, there are generally three stages:

  • Classification: These models use computer vision and natural language processing (NLP) to classify elements of a piece of content.
  • Recall and similarity search: These processes assemble different categories of like features between items or objects.
  • Ranking: The recommender orders items by relevance, often using Wide & Deep or DLRM deep learning models.

The Right Infrastructure for Recommendation Systems

Recommendation training and inference workloads most often run on data center servers, either on-premises or in the cloud. When designing and deploying recommendation systems, the main challenges that system builders and solution providers face involve increasing workload density, balancing utilization, and accelerating time to results. Software engineers will be the most challenged with returning accurate, relevant, and speedy results to end users while optimizing their code for efficient operations, both in the cloud and on-premises.

Intel Powers AI Recommenders at Every Stage and at Any Scale

To solve these recommendation system challenges and help you go from concept to production faster throughout the enterprise, Intel provides more than architecture. Globally, we employ a large roster of experienced software developers, many of whom specialize in AI, including recommender systems. Intel also brings a rare advantage by being able to combine hardware and software expertise to optimize AI recommender systems on tuned platforms.

These enhancements are already delivering significant boosts to recommendation systems for businesses globally:

Read how Intel® AI optimizations increased Taboola ad recommendation performance by 2.5x

Read how Intel® AI optimizations accelerated Yahoo! Japan Shopping recommendations by 3.5x

Developers and system builders can benefit from well-understood recipes for success in the form of reference architectures and Intel-optimized versions of popular frameworks such as PyTorch and TensorFlow. Businesses can benefit with fast time to deployment, improved customer or user engagement, and higher sales or ad revenue.

Broad Recommendation System Hardware Matches Compute with Your Needs

Following data preparation, AI recommender systems are implemented in two stages: model training and deployment inference—with many steps included therein. Architecture requirements are different between stages. Model training is faster with more parallelization, but optimizing models is less computationally demanding. Once deployed, recommender inference runs efficiently on high-core-count, high-memory-capacity CPUs that are built to manage large models and massive data.

  • Recommender AI model training: Both the Habana® Gaudi® processor and the Intel® Max Series GPU support high parallelization for fast AI model training workloads.
  • Recommender AI model training and inference: Intel® Xeon® Scalable processors are the best CPUs for AI training and inference1, and are readily available in the public cloud.
  • Recommender AI acceleration: The latest-generation Intel® Xeon® Scalable processors also feature integrated Intel® AI Engines to help speed time to results without additional hardware. These engines can help boost AI performance while lowering hardware requirements, resulting in reduced TCO. The AI tuning guide is a useful walk-through for taking advantage of these built-in AI accelerators.
  • Integrated security features for recommender workloads: Only Intel provides innovative security capabilities such as Intel® Software Guard Extensions (Intel® SGX) that help safeguard data in memory, giving you extra layers of protection, especially for recommender systems in multitenant cloud environments. A security perspective that starts from the hardware layer up will help organizations protect sensitive data and models and help comply with privacy regulations.

Build Recommendation Systems with End-to-End AI Pipeline Software

Intel optimizations of popular AI frameworks such as PyTorch and TensorFlow deliver enhanced performance on Intel® architecture that surpasses stock implementations and accelerates your time to train and deploy. Developers can access the quick start guide for instructions on how to deploy these powerful tools in just a few lines of code.

For retail use cases, Intel also provides a recommender reference kit that includes training data, models, and libraries to jump-start your project. This kit reflects the lessons learned on successful deployments combined with pretuned software packages for machine learning workloads on Intel® hardware. Developers can access the kit on GitHub.

Intel also works with several AI software vendors to optimize their performance on Intel® architectures. For a low-touch solution, visit the Intel profile on Hugging Face or the Intel® Solutions Marketplace.

Bring AI Recommendation Systems Everywhere with Intel

Intel offers community hubs and industry leadership to help you accelerate innovation for your AI recommender system design and development efforts. Access code and optimizations and get tips on relevant hardware solutions with key features that help ensure success for your AI deployments.

Join the Intel® Developer Zone, a community full of resources, software, training, and discussions

Test and measure your AI performance across a full portfolio of Intel® hardware solutions in an online sandbox


Frequently Asked Questions

A recommendation system is an AI-enabled application for serving personalized suggestions to end users for content, products, or services they may be interested in. Effective recommenders provide accurate and relevant suggestions and are responsive, fast, and cost-effective.

Recommendation systems use a chain of several AI models to perform classification, recall, and ranking across several data sets to deliver personalized results for each end user. A single system can employ up to a hundred or more distinct models.