Modes of Active Learning: Batch and Sequential Because users typically
want to see the system output something interesting immediately, a common
approach is to recompute a user’s predicted ratings after they have rated a
single item, in a sequential manner. It is also possible, however, to allow a
user to rate several items, or several features of an item before readjusting
the model. On the other hand, selecting training points sequentially has the
advantage of allowing the system to react to the data provided by users and
make necessary adjustments immediately. Though this comes at the cost of
interaction with the user at each step. Thus a trade-off exists between Batch
and Sequential AL: the usefulness of the data vs. the number of interactions
with the user.