What is Metarank?
Metarank is a recommendation and personalization service - a self-hosted reranking API to improve CTR and conversion.
Main features:
Metarank is an open-source service for:
- Algorithmic feed like on Faceook or Twitter.
- CTR-optimized category/search page ordering like on Airbnb.
- Items similar to the one you're viewing like on Amazon.
- Popular items like on any ecommerce store.
Metarank can generate recommendations based on the interaction history: clicks, likes or purchases. Personalized secondary reranking can use user and item metadata and a rich set of typical ranking feature generators to provide personalized results:
Demo
The demo itself and the data used are open-source and you can grab a copy of training events and config file in the github repo.
Let us show how you can start personalizing content with LambdaMART-based reranking in just under a minute:
- 1.
- 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.
curl -O -L https://github.com/metarank/metarank/raw/master/src/test/resources/ranklens/events/events.jsonl.gz
We will again use the configuration file from our Demo. It utilizes in-memory store, so no other dependencies are needed.
curl -O -L https://raw.githubusercontent.com/metarank/metarank/master/src/test/resources/ranklens/config.yml
With the final step we will use Metarank’s
standalone mode that combines training and running the API into one command: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
# 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}
# 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 items93363
# 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
# 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":0.8990921381835769},{"item":"103228","score":0.8990921381835769},{"item":"94864","score":0.7131600718467729},{"item":"68791","score":0.624462038351694},{"item":"72378","score":0.5269765094008626},{"item":"85131","score":0.29198666089255343},{"item":"67197","score":0.16412780810560743}]}
Last modified 1mo ago