Title: It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal],
Abstract: In this position paper, we question the current practice of calculating
evaluation metrics for recommender systems as single numbers (e.g. precision
p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express
only average effectiveness over a usually rather long period (e.g. a year or
even longer), which provides only a vague and static view of the data. We
propose that recommender-system researchers should instead calculate metrics
for time-series such as weeks or months, and plot the results in e.g. a line
chart. This way, results show how algorithms' effectiveness develops over time,
and hence the results allow drawing more meaningful conclusions about how an
algorithm will perform in the future. In this paper, we explain our reasoning,
provide an example to illustrate our reasoning and present suggestions for what
the community should do next.