I’m experimenting with purchase history as a signal for product recommendations. Curious what I’m missing.

Source: Dev.to

The problem I’m exploring

Most recommendation systems I’ve worked with or studied lean heavily on one of two things:

• Browsing behavior (clicks, views, dwell time)
• Similarity signals (category, visual similarity, embeddings)

I’ve been questioning whether historic purchase behavior might be a stronger anchor for relevance than either of those alone, especially when combined with real‑time browsing context.

Why this feels interesting (and risky)

Purchase data is:

• Sparse
• Delayed
• Messy across retailers

But it’s also the clearest expression of intent we have.

I’m trying to understand

• Does anchoring recommendations on purchase history meaningfully improve relevance?
• Where does this break down at small scale?
• At what point does recency matter more than history?
• How do you avoid overfitting someone to who they were versus who they’re becoming?

What I’m not doing

• I’m not selling anything.
• I’m not claiming this is the right approach.
• I’m not optimizing for growth yet.

This is still very much an exploration of signal quality and system design, not a polished product.

What I’d love feedback on

If you’ve worked on recommendation systems, personalization, or ecommerce tooling:

• What signals ended up being more valuable than you expected?
• What signals looked promising but failed in practice?
• How do you think about balancing long‑term behavior vs. in‑session intent?
• Are there obvious pitfalls I should be pressure‑testing earlier?

Happy to learn from anyone who’s been down this path before. Even strong skepticism is useful here.

Thanks for reading.