CLEF NewsREEL 2017 Overview: Contextual Bandit News Recommendation

In the CLEF NewsREEL 2017 challenge, we build a delegation model based on the contextual bandit algorithm. Our goal is to investigate whether a bandit approach combined with context extracted from the user side, from the item side and from user-item interaction can help choose the appropriate recommender from a recommender algorithm pool for the...

Towards Minimal Necessary Data: The Case for Analyzing Training Data Requirements of Recommender Algorithms

This paper states the case for the principle of minimal necessary data: If two recommender algorithms achieve the same effectiveness, the better algorithm is the one that requires less user data. Applying this principle involves carrying out training data requirements analysis, which we argue should be adopted as best practice for the...