Your users already know what they want. Your product just can't see it yet.
Recommend is real-time personalization infrastructure. Drop it into your stack, feed it behavioral events, and watch session depth, CTR, and revenue per user compound — without hiring an ML team.
The Cold Start Problem
is not a data problem.
Every in-house recommendation system dies in the first 50 interactions. You have no signal. The model serves noise. The user churns before you've collected enough data to course-correct. This is not a solvable problem with more data — it's an architecture problem.
Recommend ships with pre-trained cross-domain embeddings across 14 verticals. A new user on your e-commerce platform inherits behavioral priors from 847B events. Their first session isn't cold — it's warm from day one.
user.getRecommendations(newUserId) // → warm, not cold
* Quality measured as nDCG@10 across 12M user sessions. Recommend warm-start using cross-domain embedding transfer. Algolia Personalization measured using default configuration.
Collaborative vs. Content-Based
is the wrong question.
Product engineers spend months debating architecture. Meanwhile, the hybrid approach — tuned dynamically per user segment, per vertical, per time-of-day — delivers 25% higher AUC-PR than either method alone. The debate is settled.
Collaborative Filtering
Content-Based
Recommend Hybrid
* Benchmark: 1M req/s sustained load · AWS us-east-1 · p50/p99 measured over 72h window
Real-Time Feature Stores
are where latency dies.
Your recommendation pipeline is only as fast as its slowest feature fetch. In-house builds stitch together Redis, PostgreSQL, and a feature computation layer that adds 80–140ms before inference even starts. At that latency, you've already lost the scroll.
Recommend's feature store is co-located with the inference engine on the same hardware. Feature fetch is a memory read, not a network call. The entire pipeline — event ingestion to ranked response — completes in 6.2ms at p50.
A/B Testing at Scale
where your loss curve finally drops.
Most teams run one experiment at a time. Recommend runs 64 concurrent bandits, reallocating traffic to winners in real time. Statistical significance in days, not months. Your recommendation layer becomes a compounding asset.
You've seen the benchmarks.
Now run them against your data.
The sandbox ingests a sample event stream from your product, trains a model in 90 seconds, and serves live recommendations. No credit card. No sales call. Just your data, our engine.
▶ Try the Sandbox — FreeNot ready to touch code?
Get the full whitepaper: 48 pages on recommendation infrastructure, cold start benchmarks, and migration playbooks.