The aim of this study is to survey reading habits of users of an online news portal. The assumption motivating this study is that insight into the reading habits of users can be helpful to design better news recommendation systems. We estimated the transition probabilities that users who read an article of one news category will move to read an article of another (not necessarily distinct) news category. For this, we analyzed the users' click behavior within plista data set. Key findings are the popularity of category local, loyalty of readers to the same category, observing similar results when addressing enforced click streams, and the case that click behavior is highly influenced by the news category.
In time series mining, the Dynamic Time Warping (DTW) distance is a commonly and widely used similarity measure. Since the computational complexity of the DTW distance is quadratic, various kinds of warping constraints, lower bounds and abstractions have been developed to speed up time series mining under DTW distance. In this contribution, we propose a novel Lucky Time Warping (LTW) distance, with linear time and space complexity, which uses a greedy algorithm to accelerate distance calculations for nearest neighbor classification. The results show that, compared to the Euclidean distance (ED) and (un)constrained DTW distance, our LTW distance trades classification accuracy against computational cost reasonably well, and therefore can be used as a fast alternative for nearest neighbor time series classification.
Under the assumptions that (i) gamification consists of various types of users that experience game design elements differently; and (ii) gamification is deployed in order to achieve some goal in the broadest sense, we pose the gamification problem as that of assigning each user a game design element that maximizes their expected contribution in order to achieve that goal. We show that this problem reduces to a statistical learning problem and suggest matrix factorization as one solution when user interaction data is given. The hypothesis is that predictive models as intelligent tools for supporting users in decision-making may also have potential to support the design process in gamification.
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