Paper Info
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 algorithmsu0027 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.
Year
Venue
Field
2017
arXiv: Information Retrieval
Recommender system,Data mining,Computer science,Line chart,Position paper,Algorithm,Mean absolute error
DocType
Volume
Citations 
Journal
abs/1708.08447
0
PageRank 
References 
Authors
0.34
16
1
Name
Order
Citations
PageRank
Jöran Beel1659.60