Advanced Factorization Models for Recommender Systems

Recommender Systems have become a crucial tool to serve personalized content and to promote online products and media, but also to recommend restaurants, events, news and dating profiles. The underlying algorithms have a significant impact on the quality of recommendations and have been the subject of many studies in the last two decades. In...

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...

A Survey of State-of-the-Art Methods on Question Classification

The task of question classification (QC) is to predict the entity type of a question which is written in natural language. This is done by classifying the question to a category from a set of predefined categories. Question classification is an important component of question answering systems and it attracted a notable amount of research since...