Subject
11 entries
Learn to Rank
Bookmarks
Metarank: ML-Powered Ranking Engine
Metarank is an open-source ML-powered ranking engine — takes user feedback signals (clicks, purchases, bookmarks) and trains a Learn-to-Rank model to personalize product listings and search results. Low-code alternative to building a custom LTR pipeline.
Using Machine Learning to Rank Search Results (Part 2)
A practical tutorial on applying machine learning to re-rank search results — part 2 of a series covering feature engineering, training data construction, and evaluation for LTR in production search. Hands-on complement to the more theoretical LTR literature.
Test Driving Elasticsearch Learning to Rank with a Linear Model
OpenSource Connections' hands-on tutorial for the Elasticsearch Learning to Rank plugin with a linear model — walks through feature logging, model training, and deployment. The entry point for adding ML-powered ranking to an existing Elasticsearch stack.
Solr-LTR: From Zero to Learning to Rank in Apache Solr
A practical guide and reference implementation for setting up learning-to-rank in Apache Solr from scratch — covers the Solr LTR plugin, feature extraction, model training, and deployment. Complement to the Elasticsearch LTR tutorials for Solr shops.
LTR with Bees: Learning to Rank in Apache Solr
Christine Poerschke's tutorial using a bee-related dataset to demonstrate learning-to-rank in Apache Solr — end-to-end example from feature extraction through model training to serving. Concrete and dataset-grounded, unlike most abstract LTR tutorials.
Learning to Rank with Vespa
Thiago Martins' tutorial on learning-to-rank with Vespa for text search — covers how Vespa's native ML integration makes LTR simpler than Elasticsearch/Solr plugins, with models evaluated inside the search engine. Vespa's approach to embedding LTR natively.
Learning to Rank for Personalised Search (Yandex Kaggle Competition)
Yanir Seroussi's Kaggle competition post-mortem on Yandex Search Personalisation — applying learning-to-rank techniques to personalized search with behavioral signals. A practical case study of LTR on real search logs.
Learning to Rank 101: Linear Models
OpenSource Connections' foundational explainer on linear models for learning-to-rank — the first step before gradient boosted trees. Covers feature engineering and the intuition for why linear LTR models are both a useful starting point and a useful baseline.
Is Your Infrastructure Ready for Learning to Rank?
OpenSource Connections' guide to the infrastructure required before you can deploy learning-to-rank — covering the judgment lists, feature logging pipelines, training data management, and model versioning that most LTR tutorials skip. The operational reality of LTR in production.
Intuitive Explanation of Learning to Rank: RankNet, LambdaRank, and LambdaMART
Nikhil Dandekar's intuitive explainer on the evolution from pointwise to pairwise to listwise learning-to-rank, covering RankNet, LambdaRank, and LambdaMART. One of the best conceptual introductions to how modern search ranking algorithms actually work.
Introduction to Personalized Search
Recombee's introduction to personalized search — connecting the recommendation systems world to search, covering how behavioral signals (clicks, purchases) can be used to personalize result ranking per user. Bridges the gap between generic LTR and user-specific personalization.