"Running a logistic regression might be too informed a baseline and can become a long drawn process."
What does this mean? To me it implies logistic regression is already so good that its hard to see the difference between it and most recommender systems. In which case, unless you're operating at massive scale, you can just go with logistic regression
Depending on the size of the company (Revenue), and where / how the recommender system sits within the customer experience; Logistic regression could be fine. An even better baseline if discovery isn't so important for a good CX is to go with most popular and or a vendor. Building these systems used to be about getting lots of different things to work together which eats up a lot of time, and capacity (Product, UX, Eng) and it may not get the appropriate return.
Based on the advices and the feedbacks received about our datas and evidences, we added syntactic sugars to our codes, which is sure to make us more moneys. Thank you for your hard works.
I think it's strange recommendation systems don't ask more about the context.
The answer to 'How do you feel today' can result in a total different playlist.
Or a python programmer who also likes snakes might want to set the context before a search (in DDG you can).