I wonder whether a really good experience can be built by abstracting out recommendations to this degree. I think in the context of an actual product, there would be other considerations specific to the particular domain, which is partly why organizations often eventually grow up teams around recommendations or discovery.

For example:

- Perhaps there are relationships or similarities among items which are known prior to any feedback (e.g. different options of the "same" product are represented as distinct items in your catalog). Recommending to the user many closely related items may be a poor experience in some contexts.

- Perhaps you have other relevant actions, and care about more than maximizing the probability of a click. E.g. purchases, shares, etc. In an ecommerce context, you may want to recommend items at a range of price points; showing only very expensive items may get you clicks but not purchases.

- This has a concept of whether an item was "read" (which I interpret as an impression, or an opportunity to be clicked). But not all impressions are equal. Perhaps you have other knowledge about the context in which something was displayed. Was it "read" in the context of a search? Was it 8th in a list of 10 items?