  Popularity is often included in experimental evaluation to provide a
reference performance for a recommendation task. To understand how popularity
baseline is defined and evaluated, we sample 12 papers from top-tier
conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits.
We note that the widely adopted MostPop baseline simply ranks items based on
the number of interactions in the training data. We argue that the current
evaluation of popularity (i) does not reflect the popular items at the time
when a user interacts with the system, and (ii) may recommend items released
after a user's last interaction with the system. On the widely used MovieLens
dataset, we show that the performance of popularity could be significantly
improved by 70% or more, if we consider the popular items at the time point
when a user interacts with the system. We further show that, on MovieLens
dataset, the users having lower tendencies on movies tend to follow the crowd
and rate more popular movies. Movie lovers who rate a large number of movies,
rate movies based on their own preferences and interests. Through this study,
we call for a re-visit of the popularity baseline in recommender system to
better reflect its effectiveness.
