BL
B. Loni
12 records found
1
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 ha
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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
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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, w
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Pairwise learning-to-rank algorithms have been shown to allow recommendersystems to leverage unary user feedback. We proposeMulti-feedback Bayesian Personalized Ranking (MF-BPR), a pairwisemethod that exploits different types of feedback with an extendedsampling method. The feedb
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Crowdsourcing and Human computation have enabled industry and
scientists to create innovative solutions by harnessing organised
collective human effort. In human computation platforms, it is
observed that workers spend large amount of time searching for
appropriate tasks due to l
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