  Recommender systems have been investigated for many years, with the aim of
generating the most accurate recommendations possible. However, available data
about new users is often insufficient, leading to inaccurate recommendations;
an issue that is known as the cold-start problem. A solution can be active
learning. Active learning strategies proactively select items and ask users to
rate these. This way, detailed user preferences can be acquired and as a
result, more accurate recommendations can be offered to the user. In this
study, we compare five active learning algorithms, combined with three
different predictor algorithms, which are used to estimate to what extent the
user would like the item that is asked to rate. In addition, two modes are
tested for selecting the items: batch mode (all items at once), and sequential
mode (the items one by one). Evaluation of the recommender in terms of rating
prediction, decision support, and the ranking of items, showed that sequential
mode produces the most accurate recommendations for dense data sets.
Differences between the active learning algorithms are small. For most active
learners, the best predictor turned out to be FunkSVD in combination with
sequential mode.
