一种低代码机器学习对文章、列表、搜索结果和推荐进行个性化排名服务，可提高用户参与度.zip

<h1 align="center"> <a style="text-decoration: none" href="https://www.metarank.ai"> <img width="120" src="https://raw.githubusercontent.com/metarank/metarank/master/doc/img/logo.svg" /> <p align="center">Metarank: real time personalization as a service</p> </a> </h1> <h2 align="center"> <a href="https://docs.metarank.ai">Docs</a> | <a href="https://metarank.ai">Website</a> | <a href="https://metarank.ai/slack">Community Slack</a> | <a href="https://blog.metarank.ai">Blog</a> | <a href="https://demo.metarank.ai">Demo</a> </h2> [](https://github.com/metarank/metarank/actions) [](https://opensource.org/licenses/Apache-2.0)   [](https://metarank.ai/slack) ## What is Metarank? Metarank is a recommendation and personalization service - a self-hosted reranking API to improve CTR and conversion. Main features: * Recommendations: [trending](configuration/recommendations/trending.md) and [similar-items](configuration/recommendations/trending.md) (MF ALS). * Personalization: [secondary reranking](quickstart/quickstart.md) (LambdaMART) * A/B testing, [multiple model serving](configuration/overview.md#models) * [Bootstrapping](quickstart/quickstart.md#quickstart) on historical traffic data ## Common use-cases Metarank is an open-source service for: * Algorithmic feed like on FB/Twitter. * CTR-optimized category/search page ordering on Airbnb. * Items similar to the one you're viewing on Amazon. * Popular items on any ecommerce store. Metarank's recommendations are based on interaction history (like clicks and purchases), and secondary reranking - on user & item metadata and a rich set of typical ranking feature generators: * [User-Agent](configuration/features/user-session.md#user-agent-field-extractor), [Referer](configuration/features/user-session.md#referer) field parsers * [Counters](configuration/features/counters.md#counters), [rolling window counters](configuration/features/counters.md#windowed-counter), [rates](configuration/features/counters.md#rate) (CTR & conversion) * [categorical](configuration/features/scalar.md#index-vs-one-hot-what-to-choose) (with one-hot, label and XGBoost/LightGBM native encodings) * [text matching](configuration/features/text.md#fieldmatch) (ngrams and Lucene-based) * and [many more](configuration/feature-extractors.md)! ## Demo You can play with Metarank demo on [demo.metarank.ai](https://demo.metarank.ai):  The demo itself and [the data used](https://github.com/metarank/msrd) are open-source and you can grab a copy of training events and config file [in the github repo](https://github.com/metarank/metarank/tree/master/src/test/resources/ranklens). ## Metarank in One Minute Let us show how you can start personalizing content with LambdaMART-based reranking in just under a minute: 1. Prepare the data: we will get the dataset and config file from the [demo.metarank.ai](https://demo.metarank.ai) 2. Start Metarank in a standalone mode: it will import the data, train the ML model and start the API. 3. Send a couple of requests to the API. ### Step 1: Prepare data We will use the [ranklens dataset](https://github.com/metarank/ranklens), which is used in our [Demo](https://demo.metarank.ai), so just download the data file ```bash curl -O -L https://github.com/metarank/metarank/raw/master/src/test/resources/ranklens/events/events.jsonl.gz ``` ### Step 2: Prepare configuration file We will again use the configuration file from our [Demo](https://demo.metarank.ai). It utilizes in-memory store, so no other dependencies are needed. ```bash curl -O -L https://raw.githubusercontent.com/metarank/metarank/master/src/test/resources/ranklens/config.yml ``` ### Step 3: Start Metarank! With the final step we will use Metarank’s `standalone` mode that combines training and running the API into one command: ```bash docker run -i -t -p 8080:8080 -v $(pwd):/opt/metarank metarank/metarank:latest standalone --config /opt/metarank/config.yml --data /opt/metarank/events.jsonl.gz ``` You will see some useful output while Metarank is starting and grinding through the data. Once this is done, you can send requests to `localhost:8080` to get personalized results. Here we will interact with several movies by clicking on one of them and observing the results. > First, let's see the initial output provided by Metarank without before we interact with it ```bash # get initial ranking for some items curl http://localhost:8080/rank/xgboost \ -d '{ "event": "ranking", "id": "id1", "items": [ {"id":"72998"}, {"id":"67197"}, {"id":"77561"}, {"id":"68358"}, {"id":"79132"}, {"id":"103228"}, {"id":"72378"}, {"id":"85131"}, {"id":"94864"}, {"id":"68791"}, {"id":"93363"}, {"id":"112623"} ], "user": "alice", "session": "alice1", "timestamp": 1661431886711 }' # {"item":"72998","score":0.9602446652021992},{"item":"79132","score":0.7819134441404151},{"item":"68358","score":0.33377910321385645},{"item":"112623","score":0.32591281190727805},{"item":"103228","score":0.31640256043322723},{"item":"77561","score":0.3040782705414116},{"item":"94864","score":0.17659007036183608},{"item":"72378","score":0.06164568676567339},{"item":"93363","score":0.058120639770243385},{"item":"68791","score":0.026919880032451306},{"item":"85131","score":-0.35794106000271037},{"item":"67197","score":-0.48735167237049154} ``` ```bash # tell Metarank which items were presented to the user and in which order from the previous request # optionally, we can include the score calculated by Metarank or your internal retrieval system curl http://localhost:8080/feedback \ -d '{ "event": "ranking", "fields": [], "id": "test-ranking", "items": [ {"id":"72998","score":0.9602446652021992},{"id":"79132","score":0.7819134441404151},{"id":"68358","score":0.33377910321385645}, {"id":"112623","score":0.32591281190727805},{"id":"103228","score":0.31640256043322723},{"id":"77561","score":0.3040782705414116}, {"id":"94864","score":0.17659007036183608},{"id":"72378","score":0.06164568676567339},{"id":"93363","score":0.058120639770243385}, {"id":"68791","score":0.026919880032451306},{"id":"85131","score":-0.35794106000271037},{"id":"67197","score":-0.48735167237049154} ], "user": "test2", "session": "test2", "timestamp": 1661431888711 }' ``` > Now, let's intereact with the items `93363` ```bash # click on the item with id 93363 curl http://localhost:8080/feedback \ -d '{ "event": "interaction", "type": "click", "fields": [], "id": "test-interaction", "ranking": "test-ranking", "item": "93363", "user": "test", "session": "test", "timestamp": 1661431890711 }' ``` > Now, Metarank will personalize the items, the order of the items in the response will be different ```bash # personalize the same list of items # they will be returned in a different order by Metarank curl http://localhost:8080/rank/xgboost \ -d '{ "event": "ranking", "fields": [], "id": "test-personalized", "items": [ {"id":"72998"}, {"id":"67197"}, {"id":"77561"}, {"id":"68358"}, {"id":"79132"}, {"id":"103228"}, {"id":"72378"}, {"id":"85131"}, {"id":"94864"}, {"id":"68791"}, {"id":"93363"}, {"id":"112623"} ], "user": "test", "session": "test", "timestamp": 1661431892711 }' # {"items":[{"item":"93363","score":2.2013986484185124},{"item":"72998","score":1.1542776301073876},{"item":"68358","score":0.9828904282341605},{"item":"112623","score":0.9521647429731446},{"item":"79132","score":0.9258841742518286},{"item":"77561","score":0.8990