In 5G networks, Mobile Edge Computing (MEC) has been proposed to enable computation and storage capabilities at the edge of Radio Access Networks. Proactive content caching in MEC is crucial to guarantee users' Quality of Experience thanks to the reduction of traffic latency. Predicting content popularity plays a key role in the effectiveness of proactive caching. In this paper, we propose a generic and flexible recommendation framework which allows recommending suitable learning and prediction algorithms among available ones, in order to predict content popularity. The investigated algorithms are categorized into two main classes: tree-based regressors and recurrent neural networks. Through the study case of YouTube video solicitation profiles, our proposed method, called Imputation-Boosted Collaborative-Filtering based Recommending Prediction Method (IBCF-RPM) shows its effectiveness in the prediction of content popularity for various popularity profiles. By running only 30% of the prediction algorithms, randomly chosen, on a given content profile, the proposed recommending method is able to estimate the accuracy of the other predictors and recommend a well-suited predictor for content popularity.