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.