Recommendation systems
Recommendation systems reading path
A focused path through collaborative filtering, cold-start, quality metrics, ranking, richer signals, and AI-mediated product discovery.
Start with the reference pages: what a recommendation engine is and how to choose one, the recommendation engine API, and the NSL Recommender Leaderboard, which measures complete recommender systems on ten public datasets with the full methodology published.
By industry: streaming, news and publishing, ecommerce. Or compare the products in the category.
Learn ALS collaborative filtering for implicit feedback: the weighted objective, Alternating Least Squares training, cold-start limits, and production implementation.
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.
Launch personalized product discovery with retrieval, ranking, rules, analytics, and experiments—without building and operating the complete stack in-house.
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.