Robert Muir writes:
Over the summer I served as a Google Summer of Code mentor for David Nemeskey, PhD student at Eötvös Loránd University. David proposed to improve Lucene’s scoring architecture and implement some state-of-the-art ranking models with the new framework.
These improvements are now committed to Lucene’s trunk: you can use these models in tandem with all of Lucene’s features (boosts, slops, explanations, etc) and queries (term, phrase, spans, etc). A JIRA issue has been created to make it easy to use these models from Solr’s schema.xml.
Relevance ranking is the heart of the search engine, and I hope the additional models and flexibility will improve the user experience for Lucene: whether you’ve been frustrated with tuning TF/IDF weights and find an alternative model works better for your case, found it difficult to integrate custom logic that your application needs, or just want to experiment.
I count a baker’s dozen or so new features described in this post.