Search personalization and diversification are often seen as oppos-ing alternatives to cope with query uncertainty, where, given an ambiguous query, it is either preferable to adapt the search result to a specific aspect that may interest the user (personalization) or to regard multiple aspects in order to maximize the probability that some query aspect is relevant to the user (diversification). In this work, we question this antagonistic view, and hypothesize that these two directions may in fact be effectively combined and enhance each other. We research the introduction of the user as an explicit random variable in state of the art diversification methods, thus developing a generalized framework for personalized diversi-fication. In order to evaluate our hypothesis, we conduct an evalu-ation with real users using crowdsourcing services. The obtained results suggest that the combination of personalization and diver-sification achieves competitive performance, improving the base-line, plain personalization, and plain diversification approaches in terms of both diversity and accuracy measures.