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BEATDAPP RECSYS • RECOMMENDATION ENGINE

Discovery,
engineered.

A real-time recommendation engine for content-rich platforms — built by the team that runs personalization for the world’s biggest music catalogs.

RECOMMENDATION ENGINE

Taste graph

Synced
Taste graph mapping a user to their top recommended picks

CATALOG

42,318

ACTIVE USERS

186K

Built for Content-Rich, Taste-Driven Platforms

  • Music & Audio
  • Video/VOD
  • Podcasts
  • News & Media
  • Retail & Marketplaces
WHY BEATDAPP RECSYS

The recommendation engine for platforms that take discovery seriously.

We've spent a decade making personalization actually work for the world's largest music platforms. Recsys is that engine, productized for product managers and tech teams who want full control — not a black box.

FULL CONTROL

Tune every result. Not just the model.

Decide what gets recommended, what gets boosted, and what gets hidden — per surface, per audience, per moment. The engine is opinionated; you stay in charge.

Freshness, diversity and long-tail tuning sliders
  • Custom filters, business rules and boosters on every request
  • Different behaviour for home, browse, email and embedded slots
  • A/B test new logic against your current setup from day one

REAL-TIME AI

Personalization that learns as users browse.

Every play, skip, click and dwell event tunes the model in real time. No nightly retrains, no stale segments — the next request already reflects what just happened.

Real-time personalization signal
  • Collaborative + content-based signals blended automatically
  • Cold-start handled with metadata-driven embeddings
  • Re-ranks with diversity, freshness and recency built in

BUILT FOR THE LONG TAIL

Made for large, messy, taste-driven catalogs.

You have ten thousand items or ten million, dirty metadata, a sparse interaction matrix. We pick up where popularity sort and SQL rules give up.

Long-tail catalog distribution — 88% of catalog
  • Handles sparse data and inconsistent metadata gracefully
  • Surfaces the long tail without sacrificing relevance
  • A human team to guide catalog curation when it matters
HOW IT WORKS

From your raw events to
ranked recommendations across every surface.

01

Stream your events

Pipe interactions from Segment, Snowflake, Kafka or a simple webhook. We handle clicks, plays, dwell, completes, skips, saves.

Event stream
02

We learn the taste graph

Embeddings combine collaborative + content signals from your catalog metadata. Updates continuously — every interaction tunes the model.

Taste graph of related nodes
03

Ask for a ranked list

Hit one endpoint with a user and a context. Get a ranked list back — re-ranked with diversity, freshness, and your own business rules applied.

Ranked recommendation list with scores
04

Test, tune, repeat

Run A/B tests against your current logic. Our data scientists help you read the results and squeeze more lift out of every surface.

A/B test lift of variant B over A
REQUEST A DEMO

Stop sending users
to nothing.

Tell us about your catalog. We'll come back with a tailored demo and a free benchmark of your current recommendation logic against Recsys.

Request a Demo