AI recommendation systems: Real-time infrastructure for personalized experiences

Recommendation systems are everywhere: Netflix suggesting your next binge, Amazon predicting what you'll buy, Spotify curating your weekly playlist. Under the hood, they're machine learning pipelines processing millions of interactions to deliver personalized suggestions in milliseconds.

If you're building recommendation systems that need to feel instant, infrastructure choices matter. Traditional databases can add latency that makes real-time recommendations difficult. Real-time recommendation systems commonly target sub-100ms p99 latency for a snappy user experience, especially in interactive consumer apps, which means your infrastructure needs to handle vector search, real-time feature updates, and query serving without breaking a sweat.

This guide covers how recommendation systems work under the hood, the infrastructure requirements for real-time serving, and how to build a production-ready pipeline from embeddings to deployment.

What is an AI recommendation system?

AI recommendation systems are information filtering systems that use machine learning to analyze user behavior and suggest relevant items from large catalogs. They transform the overwhelming problem of choice (millions of products, songs, or videos) into a curated set of options tailored to individual preferences.

Core filtering approaches

The technical foundation relies on three core approaches. Collaborative filtering predicts what you'll like based on patterns from similar users. If users who love sci-fi also tend to watch documentaries, the system connects those dots. Content-based filtering recommends items similar to what you've already engaged with by analyzing item features and attributes. Most production systems use hybrid methods that combine both techniques. Netflix's recommendation engine blends collaborative signals with content features to deliver suggestions that balance familiarity with discovery.

Vector embeddings & similarity search

These approaches share a common foundation: vector embeddings. Vector embeddings are numerical representations of users and items in high-dimensional space. When you rate a movie or click a product, the system encodes those interactions as vector embeddings. Items that users engage with together cluster close in this mathematical space, making similarity search a core operation.

Two distance metrics appear most often in recommendation systems. Cosine similarity measures the angle between vectors, focusing on direction rather than magnitude. This works well for semantic similarity where you care about orientation rather than scale. Dot product similarity accounts for both angle and magnitude, which can matter when your model encodes confidence or strength in the vector norm.

The algorithms are mature. The challenge is making these approaches run fast enough that recommendations feel instantaneous. That's where infrastructure matters.

Why AI recommendation systems matter across industries

Recommendation systems moved from nice-to-have features to mission-critical infrastructure. AI-powered recommendations now influence 19% of all e-commerce orders—during the 2024 holiday season alone, personalized suggestions drove $229 billion in global online sales. E-commerce led the way, but recommendations now power everything from content discovery on streaming platforms to adaptive gameplay in gaming to personalized financial products in banking.

The stakes keep rising because user expectations keep rising. When your competitor delivers instant, personalized suggestions and you're showing generic lists, users notice. Response times that felt acceptable five years ago now feel sluggish.

Types of AI recommendation systems you should know

Four main approaches power modern recommendation systems. Each has distinct strengths and trade-offs, and most production systems combine multiple techniques.

• Collaborative filtering predicts preferences based on patterns from similar users, using matrix factorization to discover unexpected correlations (users who buy camping gear often purchase photography equipment), but struggles with cold start problems for new users or items.
• Content-based filtering recommends items similar to what you've engaged with previously by analyzing item features and attributes, handling new items immediately as long as metadata exists, though it can create filter bubbles that reduce discovery.
• Hybrid approaches combine collaborative and content-based techniques to address data sparsity and cold start problems more effectively, as seen in Netflix's production system that blends collaborative signals with content features and contextual information.
• Deep learning-based recommenders use neural networks like neural collaborative filtering to learn complex non-linear patterns, though Google's research found these approaches "too costly to use for item recommendation in production environments" for most use cases.

For most teams, matrix factorization with hybrid enhancements offers the best balance of accuracy and operational simplicity. Deep learning makes sense when you have the data volume and infrastructure to justify the added complexity.

Real-time infrastructure requirements & challenges

Real-time recommendations demand infrastructure that can retrieve, score, and serve results within tight latency budgets. Here's what that looks like in practice.

The latency gap

Object storage and disaggregated designs can add significant latency, so serving-time retrieval usually needs an index optimized for low-latency search. That gap matters for user experience. Batch processing tolerates higher latency, but real-time recommendation serving often targets sub-100ms p99 latency to maintain acceptable response times.

How companies build at scale

Companies building at scale invest heavily in real-time infrastructure. DoorDash's feature framework focuses on making features easier to configure and serve at scale. Uber's two-tower embedding model powers Uber Eats recommendations, replacing thousands of city-specific models with a single global model that scales to hundreds of millions of users.

The infrastructure stack requires low-latency prediction serving that retrieves candidate items, scores them with your model, and returns ranked results within your latency budget. Production-scale architectures typically include data processing layers, real-time recommendation components with model scoring, and inference optimization with feature caching.

If your team already uses Redis for caching or session management, you have a head start—Redis now handles AI workloads too. Redis stores operational data in memory for sub-millisecond latency, and its Query Engine adds native vector search so you can store embeddings alongside that data instead of adding a separate vector database.

Embedding considerations

Vector dimension size impacts storage and query performance. The MTEB leaderboard shows that commercial embedding models often outperform open-source alternatives in benchmarks, but production teams typically find well-optimized vector embeddings at moderate dimensions (512-1024) deliver the best balance of accuracy and performance.

Getting started with AI recommendation systems

Building a recommendation system involves selecting an architecture, training models on temporal data, generating embeddings, and deploying with proper evaluation and monitoring.

Architecture & model training

Start with architecture selection. TensorFlow Recommenders provides production-focused implementations with a two-tower architecture: a query model that processes user features and a candidate model that processes item features. The framework handles data preparation, model training, evaluation, and deployment as an integrated workflow.

Your data pipeline matters more than you'd expect. Don't use random splits for recommendation systems. Temporal ordering is critical. Train on past data, validate on future data. This mirrors production reality where your model predicts future interactions based on historical patterns. Random splits create unrealistic performance estimates because they violate the temporal assumptions of production systems.

Embedding generation

Embedding generation has multiple paths. TensorFlow and PyTorch enable custom neural architectures. For rapid prototyping, Hugging Face models provide over 500 embedding options optimized for similarity search. Once you've generated vector embeddings, you need infrastructure to serve them at scale. Redis stores these vector embeddings alongside your operational data, enabling sub-millisecond vector search without adding a separate system to your stack.

Vector search & storage

Vector search infrastructure connects vector embeddings to recommendations. While libraries like FAISS work for prototyping and research, production systems need managed infrastructure that handles scaling, persistence, and hybrid queries. Redis Query Engine supports vector search alongside sub-millisecond performance for many core operations, with hybrid queries combining similarity search and metadata filtering in single queries—such as finding "similar products within this brand" for personalized recommendations.

Evaluation metrics

Evaluation requires rank-aware metrics, not just accuracy. Mean reciprocal rank measures where the first relevant item appears. Normalized Discounted Cumulative Gain (NDCG) considers both number and position of relevant items, penalizing items lower in rankings. Mean average precision accounts for precision at each relevant position. These metrics reflect user behavior better than simple accuracy since users rarely scroll past the first few recommendations.

Production rollout

Production deployment benefits from gradual rollout with monitoring. Deploy new models to small user segments first. Implement A/B testing against control groups to catch feedback loops where your model's own recommendations bias future training data. Track production metrics including data quality, feature accuracy, model performance, and system health. This incremental approach prevents model degradation from affecting your entire user base.

Redis as real-time infrastructure for AI recommendations

Redis Query Engine provides vector search with multiple index types offering different precision-performance trade-offs. FLAT delivers brute-force exact search, best suited for smaller datasets or when exact matching is required. Hierarchical Navigable Small World (HNSW) provides graph-based approximate search optimized for speed at scale. SVS-VAMANA offers another approximate search option with different performance characteristics. Each index type has configurable parameters that let you tune the precision-recall balance for your specific use case. In internal benchmarks on 1 billion 768-dimensional vectors, Redis 8 sustained 66,000 vector insertions per second for indexing configurations targeting at least 95% precision.

Unified data platform

The unified platform approach means consolidating your infrastructure. Instead of orchestrating separate systems for vectors, caching, and operational data, Redis handles all three. Caching and operational data operations deliver sub-millisecond latency, while vector search latency depends on dataset scale and precision requirements. This reduces vendor sprawl and simplifies architecture, but more importantly, it eliminates the latency overhead of network hops between separate systems.

Performance at scale

The performance characteristics matter for production systems. In Redis 8 internal benchmarks on 1 billion 768-dimensional vectors, Redis achieved about 90% precision with a median latency of ~200ms and about 95% precision at ~1.3 seconds, with 50 concurrent queries. These trade-offs are typical of approximate nearest neighbor search. You'll need to benchmark with your specific data and latency requirements.

Hybrid search queries

Hybrid search combines vector similarity with metadata filtering, enabling queries like "find similar products within this brand for users who prefer this price range." This architecture resembles hybrid recommendation approaches: vector similarity captures behavior-based relationships, while metadata filters constrain results by attributes such as brand or price. This unified approach enables complex queries without requiring separate systems:

Semantic caching for recommendations

Redis LangCache is a fully managed semantic caching service that recognizes semantically similar queries and returns cached responses in milliseconds. For recommendation systems with repetitive query patterns, this can significantly reduce backend load. Redis LangCache delivers up to 15× faster responses on cache hits. One healthcare customer reports achieving a 70% cache hit rate, saving 70% on LLM spend. The technique converts queries into vector embeddings and measures cosine similarity, returning cached responses when similarity exceeds configured thresholds.

Production example

Relevance AI's migration demonstrated these performance improvements in practice. Their vector search latency went from 2 seconds to 10 milliseconds after adopting Redis. This performance improvement helped Relevance AI fully automate their SDR workflows, with AI agents handling prospecting, cold emails, and CRM updates in real-time.

Real-time recommendations need real-time infrastructure

Recommendation systems have moved from experimental features to revenue-generating infrastructure. But delivering recommendations that feel instant requires purpose-built infrastructure. General-purpose databases often can't deliver sub-100ms latency at scale.

Redis provides that foundation. Billion-scale vector search for real-time similarity matching. Semantic caching that delivers faster query times by recognizing similar queries and returning cached responses—up to 15× faster than LLM inference on cache hits. The Redis Query Engine supports hybrid search combining vector similarity with metadata filtering in single queries. Your vector embeddings live alongside session data, feature values, and app state in one system instead of three.

If you're building recommendation systems that need to scale, try Redis free to test your vector embeddings, measure your latency, and validate your architecture. Or talk to our team about optimizing your AI infrastructure.