An insight about recommender systems from someone whose name I regretfully can't recall at the moment:

Many recommender systems/algorithms model interest as a precise static region in representation space, such that the algorithm becomes about zooming in forever and ever to higher-resolution patches of this space to find exactly what the user wants. In reality, interests shift, and the recommender algorithm may influence the user's shifting interests, in addition to being informed by them. So it makes more sense to model recommendation algorithms as a thing that traverses a linked list or a branching tree evolving over time, more than a "zooming in forever" into some perfectly interest-aligned patch of the topic space.