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We describe in this paper our approach for the Multi-modal gesture recognition challenge organized by ChaLearn in conjunction with the ICMI 2013 conference. The competition's task was to learn a vocabulary of 20 types of Italian gestures performed from different persons and to detect them in sequences. We develop an algorithm to find the gesture intervals(More)
We describe in this paper our contribution to the ECML PKDD Discovery Challenge 2013 (Offline Track). This years task was to predict the next given names a user of a name search engine interacts with. We model the user preferences with a sequential factor model that we optimize with respect to the Bayesian Personalized Ranking (BPR) Optimization Criterion.(More)
Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections(More)
In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers(More)
Preface All over the world, future parents are facing the task of finding a suitable given name for their children. Their choice is usually influenced by a variety of factors, such as the social context, language, cultural background and especially personal taste. Although this task is omnipresent, little research has been conducted on the analysis and(More)
Bayesian Inference with Markov Chain Monte Carlo (MCMC) has been shown to provide high prediction quality in recommender systems. The advantage over learning methods such as coordinate descent/alternating least-squares (ALS) or (stochastic) gradient descent (SGD) is that MCMC takes uncertainty into account and moreover MCMC can easily integrate priors to(More)
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