Changing behaviour of an ML model
This is a kind of confirmation bias, it's often found in recommender systems and it's hard to beat: since the system is designed to find similar books, it's normal that it returns the most similar books but it can be disappointing for the user. This bias is especially strong if there are few past books to refer to, i.e. the reference sample used by the model is too small to represent the user's preferences.
As Nikos suggested in a comment, one way to diversify the recommendations is to broaden the set of similarity features. A more advanced way is to use some second-degree approach: the system doesn't only recommend books similar to a specific user's past, but finds other users who liked the same books and recommends books that they liked (similar to &quot;other users who like X also like Y&quot;).