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metarank
A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
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Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App. With SurveyJS form UI libraries, you can build and style forms in a fully-integrated drag & drop form builder, render them in your JS app, and store form submission data in any backend, inc. PHP, ASP.NET Core, and Node.js.
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As people with heavy e-commerce background, we feel that the main pain point of typical old-school offline personalization solutions is that 80% of customers in medium-sized online stores are coming only once:
* you have a very short window to adapt your store, as the visitor will never come back in the future.
* even if you have zero past knowledge about a new visitor, there is still something to compare with other similar visitors: are they from mobile? Is it ios or android? Are they US? Is it a holiday now? Did they come from google search or facebook ad?
* this knowledge is ephemeral and makes sense only within their current session. But a visitor can still do a couple of interactions like browsing different collections of items or clicking on search results, and it can also be taken into account.
But compared to Amazon and Google, it's you who define which features should be used for the ranking and how long they are stored (see the "ttl" option on all feature extractors in our docs for details).
For example, here is https://github.com/metarank/metarank/blob/master/src/test/re... the config of features used in the movie recommendations demo - in a most privacy-sensitive setup you can just drop all the "interacted_with" extractors and will get zero private data stored for each visitor.
Right now it runs in a dev-mode on a single EC2 t3.large instance with loadavg ~0.30, but the inference load is quite tiny right now: around 3-4 reranking requests per second. And yes, as a typical open-source project it still crashes from time to time :)
The training dataset is not that huge (see https://github.com/metarank/ranklens/ for details, it's open-source), so we do a full retraining directly on the node right after the deployment, and it takes around 1 minute to finish. We also run the same process inside the CI: https://github.com/metarank/metarank/blob/master/run_e2e.sh
There is an option to run this thing in a distributed mode:
* training is done using a separate batch job running on Apache Flink (and on k8s using flink's integration)
* feature updates are done in a separate streaming Flink job, writing everything in Redis
* The API fetches latest feature values from Redis and runs the ML model.
The dev-mode I've mentioned earlier is when all these three things are bundled together in a single process to make it easier to play with the tool. But we didn't spent much time testing distributed setup, as this thing is still a hobby side-project and we're limited in time spent developing it.
Soo cool would love to see this integrated with https://github.com/medusajs/medusa
According to the code on https://github.com/MetaMask/eth-phishing-detect/blob/45ea5cf..., looks like that everything within Levenstein distance of 3 from whitelisted hosts (like "metamask.*") is blocked.
Metarank and Metamask have the distance of 3. I've made a ticket some time ago in their github repo (https://github.com/MetaMask/eth-phishing-detect/issues/6855), but it seems that it was lost in thousands of similar tickets.