Recommendations — nearest neighbours at production scale.
Embed users, items, and interactions into the same vector space. Use HNSW nearest-neighbour retrieval to power product recommendations, content feeds, and user matching — with the storage economics of object storage and the latency of a purpose-built index.
The challenge
Recommendation systems need fast, scalable ANN.
Two-stage recommenders retrieve candidates via approximate nearest-neighbour lookup, then re-rank. The ANN retrieval step needs to be fast, scalable, and cheap — properties in-memory vector databases struggle to deliver at catalog scale.
The solution
ManyVector as the candidate retrieval layer.
Store user and item embeddings in ManyVector. Retrieve the top-k nearest items for a given user vector in under 10ms. Namespaces per user segment, per A/B variant, or per content category — all from your own bucket.
Common recommendation architectures.
Item-to-item
Similar items
Embed item A and retrieve its nearest neighbours from the item namespace. Powers "customers also bought," "similar products," and "you may also like" patterns.
User-to-item
Personalized feed
Aggregate user interaction embeddings into a user vector. Retrieve nearest items from the catalog namespace. Personalization at scale without co-matrix factorization overhead.
Session-based
In-session context
Embed the current session sequence and retrieve items relevant to what the user is doing right now. Works for anonymous users with no interaction history.
Real-time personalization at scale.
<10ms
recommendation serving
Any
item type
Real-time
personalization
Filters
for business rules
Common questions
How do I compute user embeddings for personalization?
Average the embeddings of items a user has interacted with, or train a user tower on your interaction data. ManyVector stores and queries these like any other vector.
Can I filter recommendations by category or availability?
Yes. Add category, price, availability, or any attribute as metadata. Pass filter conditions alongside your vector query to enforce business rules.
How do I handle cold-start for new items with no history?
Use content-based embeddings — image features, text descriptions — for new items. These work immediately without any interaction data.
Can ManyVector serve real-time recommendation APIs?
Yes. The query API is designed for sub-10ms p50 serving. Many production recommendation systems serve directly from ManyVector behind an API gateway.