o19s/elasticsearch-learning-to-rank 排名第 10,106，此处有 8 个同类条目；Eric-mingjie/rethinking-network-pruning 排名第 10,122。

The Elasticsearch Learning to Rank plugin uses machine learning to improve search relevance ranking. It's powering search at places like Wikimedia Foundation and Snagajob! This plugin: Allows you to store features (Elasticsearch query templates) in Elasticsearch Logs features scores (relevance scores) to create a training set for offline model development Stores linear, xgboost, or ranklib ranking models in Elasticsearch that use features you've stored Ranks search results using a stored model Where's the docs? We recommend taking time to read

the docs. There's quite a bit of detailed information about learning to rank basics and how this plugin can ease learning to rank development. You can also participate in regular trainings on Elasticsearch Learning to Rank, which support the free work done on this plugin. The demo lives in another repo now, Hello LTR and it has both ES and Solr example. Follow the directions for Elasticsearch in the README to set up the environment and start with the notebooks

/elasticsearch/tmdb/hello-ltr. Installing See the full list of prebuilt versions and select the version that matches your Elasticsearch version. If you don't see a version available, see the link below for building or file a request via issues. To install, you'd run a command like this but replacing with the appropriate prebuilt version zip: (It's expected you'll confirm some security exceptions, you can pass to to automatically install) If you already are running Elasticsearch, don't forget to restart

! Known issues As any other piece of software, this plugin is not exempt from issues. Please read the known issues to learn about the current issues that we are aware of. This file might include workarounds to mitigate them when possible. Build and Deploy Locally Notes if you want to dig into the code or build for a version there's no build for, please feel free to run the build and installation process yourself: How to Contribute For more information

on helping us out (we need your help! ), developing with the plugin, creating docs, etc please read CONTRIBUTING. Elastic Release Support We do our best to officially support releases of Elasticsearch. If you have a need for "dot-oh" compatibility or a version we don't support please consider submitting a PR. Initially developed at OpenSource Connections. Significant contributions by Wikimedia Foundation, Snagajob Engineering, Bonsai, and Yelp Engineering Thanks to Jettro Coenradie for porting to ES 6. 1 Other Acknowledgments & Stuff

To Read Bloomberg's Learning to Rank work for Solr Our Berlin Buzzwords Talk, We built an Elasticsearch Learning to Rank plugin. Then came the hard part Blog article on How is Search Different from Other Machine Learning Problems Also check out our other relevance/search thingies: book Relevant Search, projects Elyzer, Splainer, and Quepid