PIE: Personalized Interest Exploration for Large-Scale Recommender Systems. (arXiv:2304.06844v1 [cs.IR])

Recommender systems are increasingly successful in recommending personalized
content to users. However, these systems often capitalize on popular content.
There is also a continuous evolution of user interests that need to be
captured, but there is no direct way to systematically explore users'
interests. This also tends to affect the overall quality of the recommendation
pipeline as training data is generated from the candidates presented to the
user. In this paper, we present a framework for exploration in large-scale
recommender …

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