• Publications
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The plista dataset
Releasing datasets has fostered research in fields such as information retrieval and recommender systems. Datasets are typically tailored for specific scenarios. In this work, we present the plistaExpand
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Increasing Diversity Through Furthest Neighbor-Based Recommendation
TLDR
In this paper we investigate to which extent an inverted nearest neighbor model, k-furthest neighbor, is suitable for complementing a traditional kNN recommender. Expand
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Recommending Personalized News in Short User Sessions
TLDR
We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Expand
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Recommender systems challenge 2012
TLDR
The Recommender System Challenge 2012 invited participants to work on two tracks with real-world datasets and to submit their contributions that would be related to specific problem contexts. Expand
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Personalizing tags: a folksonomy-like approach for recommending movies
TLDR
We present a simple way of using a priori movie data in order to improve the accuracy of collaborative filtering recommender systems by inferring personal ratings on tags. Expand
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Workshop and challenge on news recommender systems
Recommending news articles entails additional requirements to recommender systems. Such requirements include special consumption patterns, fluctuating itemcollections, and highly sparse userExpand
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Incorporating context and trends in news recommender systems
TLDR
In our fast changing world, data streams move into the focus. Expand
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Evaluation of Cross-Domain News Article Recommendations
TLDR
This thesis will investigate methods to increase the utility of news article recommendation services based on real-world user feedback. Expand
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The 2019 Multimedia for Recommender System Task: MovieREC and NewsREEL at MediaEval
TLDR
The MediaEval 2019 Task “Multimedia for Recommender Systems” investigates the potential of leveraging multimedia content to enhance recommender systems. Expand
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Benchmarking News Recommendations in a Living Lab
TLDR
We introduce a living lab for the real-time evaluation of news recommendation algorithms in real time. Expand
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