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Personalization is showing the same items but in different order for different users.
The ordering of products on Amazon, posts in FB, and search results in Google is personalized for each visitor, as it directly affects conversion, click rate and engagement, and we've done 50+ a/b tests in different ecommerce verticals to confirm it.
If you have items that are presented to a user in a specific order, you can look at personalizing this order to improve your product's KPIs.
Metarank can run on-premises or in the cloud, making it easy to get state-of-the-art ML technologies by teams having little to no ML experience.
Building an in-house personalization solution takes around 6 months by an experienced team, and only large companies can afford it.
With Metarank, you can get up and running with personalization in days instead of months and be in control of data privacy, optimization goals and how Metarank is integrated into the infrastructure with minimal vendor-lock.
We have a built a Demo which showcases how you can use Metarank in the wild.
The Demo utilizes Ranklens dataset that we have built using Toloka service to gather user interactions.
Application code can be found here and you can see how easy it is to query Metarank installation to get real-time personalization.
Check out the step-by-step Tutorial of running Metarank Demo locally.
- Technical overview of the way it can be integrated in your existing tech stack.
- Configuration walkthrough
- API overview
- Feature extractors
- CLI Options
- Running Metarank in Docker
- Contribution guide
- License
We recommend running Metarank with Docker.
If you would like to try it natively, here are the requirements:
- Linux or MacOS on x86. Windows support is coming soon
- JVM 11
This project is released under the Apache 2.0 license, as specified in the LICENSE file.