'Project smells' - Experiences in Analysing the Software Quality of ML Projects with mllint

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes...

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...

Platform for Task Recommendation in Human Computation

Crowdsourcing and Human computation have enabled industry and<br/>scientists to create innovative solutions by harnessing organised<br/>collective human effort. In human computation platforms, it is<br/>observed that workers spend large amount of time searching for<br/>appropriate tasks due to lack of effective task discovery mechanism. This...