Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture crops remains challenging due to their...
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The popularity of recommender systems has led to a large variety of their application. This, however, makes their evaluation a challenging problem, because different and often contrasting criteria are established, suc...
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The popularity of recommender systems has led to a large variety of their application. This, however, makes their evaluation a challenging problem, because different and often contrasting criteria are established, such as accuracy, robustness, and scalability. In related research, usually only condensed numeric scores such as RMSE or AUC or F-measure are used for evaluation of an algorithm on a given data set. It is obvious that these scores are insufficient to measure user satisfaction. Focussing on the requirements of business and research users, this work proposes a novel, extensible framework for the evaluation of recommender systems. In order to ease user-driven analysis we have chosen a multidimensional approach. The research framework advocates interactive visual analysis, which allows easy refining and reshaping of queries. Integrated actions such as drill-down or slice/dice, enable the user to assess the performance of recommendations in terms of business criteria such as increase in revenue, accuracy, prediction error, coverage and more. The ability of the proposed framework to comprise an effective way for evaluating recommender systems in a business- user-centric way is shown by experimental results using a research prototype.
Automating machinelearning by providing techniques that autonomously find the best algorithm, hyperparameter configuration and preprocessing is helpful for both researchers and practitioners. Therefore, it is not sur...
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In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation ...
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We participated in the MediaEval Benchmarking whose goal is to concentrate on the multimodal geo-location prediction on the Yahoo! Flickr Creative Commons 100M dataset - the placing task. It challenges participants to...
We participated in the MediaEval Benchmarking whose goal is to concentrate on the multimodal geo-location prediction on the Yahoo! Flickr Creative Commons 100M dataset - the placing task. It challenges participants to develop models and/or techniques to estimate the geographic locations of the Flickr resources based on textual metadata, e.g. titles, descriptions and tags. We aim to find a procedure that is conceptual to understand, simple to implement and flexible to integrate different techniques. In this paper, we present a three-step approach to tackle the locale-based sub-task.
Move prediction systems have always been part of strong Go programs. Recent research has revealed that taking interactions between features into account improves the performance of move predictions. In this paper, a f...
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The popularity of recommender systems has led to a large variety of their application. This, however, makes their evaluation a challenging problem, because different and often contrasting criteria are established, suc...
详细信息
The popularity of recommender systems has led to a large variety of their application. This, however, makes their evaluation a challenging problem, because different and often contrasting criteria are established, such as accuracy, robustness, and scalability. In related research, usually only condensed numeric scores such as RMSE or AUC or F-measure are used for evaluation of an algorithm on a given data set. It is obvious that these scores are insufficient to measure user satisfaction. Focussing on the requirements of business and research users, this work proposes a novel, extensible framework for the evaluation of recommender systems. In order to ease user-driven analysis we have chosen a multidimensional approach. The research framework advocates interactive visual analysis, which allows easy refining and reshaping of queries. Integrated actions such as drill-down or slice/dice, enable the user to assess the performance of recommendations in terms of business criteria such as increase in revenue, accuracy, prediction error, coverage and more. The ability of the proposed framework to comprise an effective way for evaluating recommender systems in a businessuser- centric way is shown by experimental results using a research prototype.
Suggesting tasks and learning resources of appropriate difficulty to learners is challenging. Neither should they be too difficult and nor too easy. Well-chosen tasks would enable a quick assessment of the learner, we...
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Suggesting tasks and learning resources of appropriate difficulty to learners is challenging. Neither should they be too difficult and nor too easy. Well-chosen tasks would enable a quick assessment of the learner, well-chosen learning resources would speed up the learning curve most. We connect active learning to classical pedagogical theory and propose the uncertainty sampling framework as a means to the challenge of selecting optimal tasks and learning resources to learners. To assess the efficiency of this strategy, we compared different exercise selection strategies and evaluated their effect on different datasets. We consistently find that uncertainty sampling significantly outperforms several alternative exercise selection approaches and thus leads to a faster convergence to the true assessment. These findings demonstrate that active (machine) learning is consistent with classic learning theory. It is a valuable instrument for choosing appropriate exercises as well as learning resources both from a teacher's and from a learner's perspective.
Due to its various applications, time-series classification is a prominent research topic in data mining and computational intelligence. The simple k-NN classifier using dynamic time warping (DTW) distance had been sh...
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Historically, student performance prediction has been approached with regression models. For instance, the KDD Cup 2010 used the root mean squared error (RMSE) as an evaluation criterion. This is appropriate when the ...
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Historically, student performance prediction has been approached with regression models. For instance, the KDD Cup 2010 used the root mean squared error (RMSE) as an evaluation criterion. This is appropriate when the goal is to predict student marks or how well will they perform in a given exercise. Since in many datasets the target variable is binary, i.e. a student has solved the exercise or failed in it, it would be natural to look at this problem as to a classification task. Another, probably not so usual case could be when we only have a so-called positive feedback, i.e. only the successful solutions are recorded. In this case, neither the regression nor the classification approaches would be useful and one could look on this problem as to a ranking task. We propose to look at solving the student performance prediction as a classification or ranking tasks, respectively, where models are optimized for appropriate error measures which are the Hinge loss and the Area under the ROC curve. Experimental comparison of these techniques are introduced using two, large-scale datasets. Both methods are well known in their respective fields, thus the goal of this paper is to introduce them in the educational data mining community.
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