Recommendation systems
Recommendation systems reading path
A focused path through collaborative filtering, cold-start, quality metrics, ranking, richer signals, and AI-mediated product discovery.
A technical walkthrough of ALS collaborative filtering, Alternating Least Squares, weighted implicit feedback, and the Hu-Koren-Volinsky framework behind production recommendations.
Read the foundation →
Every recommendation system has a cold-start problem. This post explains practical production strategies for user and item cold-start paths.
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.
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 practical look at how app recommendation engines power user engagement, discovery, retention, and personalised experiences.
Why overly precise recommendation models can harm user satisfaction and what to do instead.
AI-powered assistants are becoming a product discovery layer for ecommerce. That changes where relevance gets decided, who controls the shopping journey, and how recommendation systems need to adapt.
NeuronSearchLab now supports a broader retrieval and ranking stack: content embeddings, two-tower retrieval, gSASRec-style sequential retrieval, Semantic-ID generative retrieval, XGBoost rankers, MMoE, PLE, and bounded reranking.