We welcome Teresa Scheidt, who joins our group for a research internship over the summer. Teresa is a Master student at Lund University, where she is currently in the first year of the Master program ‘Machine Learning, Systems and Control’. She completed her bachelor’s degree in ‘Medizintechnik’ (Medical Engineering) at Read more…
We were accepted for publication at ComplexRec 2019, the third workshop on Recommendation in Complex Scenarios at the 13th ACM Recommender Systems Conference (RecSys 2019) in Copenhagan, Denmark. Abstract. effectiveness of recommender system algorithms varies in different real-world scenarios. It is difficult to choose a best algorithm for a scenario Read more…
Abstract. Many recommender-system datasets are pruned, i.e. some data is removed that wouldn’t be removed in a production recommender-system. For instance, the MovieLens dataset contains only data from users who rated 20 or more movies.1 Similarly, some researchers prune data themselves and conduct their experiments only on subsets of the Read more…
After announcing a guest presentation from Zalando in our e-Business lecture, we are delighted to announce another talk by Zalando, this time in our machine learning lecture. Antoaneta Marinova from Zalando, will give a presentation on 27th November at 15:00 o’clock. Antoaneta is a Data Engineer in the Customer Fashion Profile Read more…
We published a manuscript on arXiv about the first living lab for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., research papers, citations, conferences, research grants etc. Recommendations are delivered through the living lab´s API in platforms such Read more…
We published a preprint on arXiv, in which we question the current practice of calculating evaluation metrics for recommender systems as single numbers (e.g. precision p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express only average effectiveness over usually a rather long period (e.g. a Read more…