Despite the proliferation of recommendation algorithms, the question of which recommender works best for which user-item instance remains widely open. In this paper, we develop a meta-learning approach that chooses among several recommendation algorithms, which one is best suited for predicting the preference of a user for an item. We propose an empirical investigation of the meta-learner when applied to implicit and explicit datasets. The meta-learner is trained using four classifiers/regressors: logistic regression, decision trees, stochastic gradient descent, and gradient boosting. We run extensive experiments on four real datasets: RETAIL, a proprietary implicit dataset provided by our industrial partner, TAFENG, a publicly available grocery shopping dataset and two publicly available AMAZON datasets with explicit preferences. Results show that using a meta-learner yields higher accuracy than single recommendation algorithms for explicit datasets when compared to state-of-the-art ensemble-learned models and factorization machines. This work is an ongoing collaboration with the marketing department of a major industrial partner to test promotional offers for different customer segments.