Improving accuracy has traditionally been the main focus of research. Accuracy alone, however, may not be enough to entice the user with RSs. This is because the system implementing AL may also need to recommend items of high
novelty/serendipity, improve coverage, or maximize profitability, to name a few
[44, 21, 33]. Another aspect that is frequently overlooked by AL researchers is the
manner in which a user can interact with AL to reap improvements in performance.
Simply presenting items to the user for rating lacks ingenuity to say the least; surely
there is a better way? One example of this is a work [3] which demonstrated that by
using the right interface even such menial tasks as labeling images could be made
fun and exciting. With the right interface alone the utility of an AL system may
increase dramatically.
Many issues remain that must be tackled to ensure the longevity of AL in RSs;
with a little innovation and elbow grease we hope to see it transform from a “bothersome process” to an enjoyable one of self-discovery and exploration, satisfying
both the system objectives and the user at the same time.