data · open source
Recommendation & Personalisation with pgvector
Recommendation & Personalisation built on pgvector, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 6
Why pgvector for this
Cold start is where most recommendation systems disappoint, new users and new products are exactly the cases where a good recommendation matters most.
pgvector is strongest at one database for relational and vector data, with transactions across both. For recommendation & personalisation that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: at very large vector volumes a dedicated index outperforms it. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.
Six weeks to something running in production, not six quarters to a strategy document.
The honest assessment
- What it is
- Vector search inside Postgres, no separate vector database to operate.
- Strongest at
- one database for relational and vector data, with transactions across both
- Trade-off
- at very large vector volumes a dedicated index outperforms it
- Category
- data
We are not a reseller for pgvector and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Event tracking design, since most projects start with inadequate data
- Baseline popularity model to beat
- Hybrid collaborative and content-based ranking
- Cold-start handling for new users and new items
- A/B testing framework with proper statistics
- Business-metric reporting, not just offline accuracy
Questions
How much data do we need?
Less than people assume to start. A content-based approach works from day one; collaborative filtering improves as interaction volume grows.
How do you handle new products?
Content-based features carry new items until interaction data accumulates, with deliberate exploration so new items get a fair chance to be seen.
How do we know it is working?
Controlled A/B tests measured on revenue or engagement, with proper statistical treatment rather than eyeballing a dashboard.
Alternatives for recommendation & personalisation
Same capability, different stack. Each page states its own trade-off.
What else we build on pgvector
Building with pgvector?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878