  The effectiveness of recommendation algorithms is typically assessed with
evaluation metrics such as root mean square error, F1, or click through rates,
calculated over entire datasets. The best algorithm is typically chosen based
on these overall metrics. However, there is no single-best algorithm for all
users, items, and contexts. Choosing a single algorithm based on overall
evaluation results is not optimal. In this paper, we propose a
meta-learning-based approach to recommendation, which aims to select the best
algorithm for each user-item pair. We evaluate our approach using the MovieLens
100K and 1M datasets. Our approach (RMSE, 100K: 0.973; 1M: 0.908) did not
outperform the single-best algorithm, SVD++ (RMSE, 100K: 0.942; 1M: 0.887). We
also develop a distinction between meta-learners that operate per-instance
(micro-level), per-data subset (mid-level), and per-dataset (global level). Our
evaluation shows that a hypothetically perfect micro-level meta-learner would
improve RMSE by 25.5% for the MovieLens 100K and 1M datasets, compared to the
overall-best algorithms used.
