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NeuronSearchLab Blog
Insights, tutorials, and product updates on AI-powered personalisation.
Most teams track CTR and call it done. Here's what NDCG, hit rate, and catalogue coverage actually measure — and how to use them before you ship.
Every recommendation system has a cold-start problem. This post explains practical production strategies for user and item cold-start paths.
Most recommendation vendors give you a black box. We built an open evaluation harness so you can score any system — including ours — with your own data.
Recent updates from Meta, Google, retail media infrastructure providers, and agentic commerce platforms point to the same lesson: recommendation quality increasingly depends on richer feedback, stronger metadata, and production-grade retrieval rather than clicks alone.
A technical walkthrough of Alternating Least Squares matrix factorisation for implicit feedback, and how NeuronSearchLab applies it in production.
Recent platform changes suggest that search, recommendations, and advertising are no longer separate optimisation problems. They are increasingly drawing on the same retrieval, ranking, and feedback infrastructure.
Meta just announced four new chips purpose-built for ranking and recommendations. The strategic lesson applies well beyond billion-user scale.
AI chatbots are beginning to sit between shoppers and product catalogues. That shifts where relevance gets decided, and who controls it.
Recent recommendation-system research suggests that quality is no longer just a ranking problem. Reliability, reproducibility, and safety are becoming part of the evaluation baseline too.
X’s latest transparency push highlights an uncomfortable truth: recommendation quality and recommendation trust are related, but they are not the same thing.
What X’s engineering write-up revealed about candidate sourcing, ranking, and filtering - and what product teams should learn from it.
A practical look at what algorithmic feeds on X reveal about recommendation systems, ranking tradeoffs, and why operator control matters.
What the proposed TikTok-Oracle arrangement revealed about recommendation engines: they are not just product features, they are strategic infrastructure.
Why overly precise recommendation models can harm user satisfaction and what to do instead.
A look at how recommendation systems power user engagement, with examples from TikTok and beyond.
A plain-language explanation of how vectors and embeddings power modern ML recommendation systems.
A new kind of recommendation engine that gives you control, flexibility, and lightning-fast personalisation powered by machine learning.