Stefan Hagen Weber

Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations

In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative filtering. Each of these approaches requires different amounts of manual interaction. We collected a data set of reviews with corresponding ordinal (star) ratings of several thousand movies to evaluate the different features for the collaborative filtering. We employ a state-of-the-art collaborative filtering engine for the recommendations during our evaluation and compare the performance with and without using the features representing user preferences mined from the free-text reviews provided by the users. The opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.

Applicability of Full Function Points at Siemens AT

It is widely acknowledged that software metrics are a useful tool to support the decision making process in the IT industry.

AMPLIO VQA – A Web Based Visual Query Analysis System for Micro Grid Energy Mix Planning

Micro grid technology brings the opportunity to integrate renewable energies with a traditional energy mix on a regional level and to achieve specific local goals. To obtain an optimal energy mix plan, different scenarios need to be simulated and analyzed. However, effective tools for analyzing such simulation results are lacking. Here we present an interactive visual query analysis tool designed for that purpose. The tool integrates effective visualization techniques and advanced pattern detection methods.

Commercial Visual Analytics Systems-Advances in the Big Data Analytics Field

Five years after the first state-of-the-art report on Commercial Visual Analytics Systems we present a reevaluation of the Big Data Analytics field. We build on the success of the 2012 survey, which was influential even beyond the boundaries of the InfoVis and Visual Analytics (VA) community. While the field has matured significantly since the original survey, we find that innovation and research-driven development are increasingly sacrificed to satisfy a wide range of user groups. We evaluate new product versions on established evaluation criteria, such as available features, performance, and usability, to extend on and assure comparability with the previous survey. We also investigate previously unavailable products to paint a more complete picture of the commercial VA landscape. Furthermore, we introduce novel measures, like suitability for specific user groups and the ability to handle complex data types, and undertake a new case study to highlight innovative features. We explore the achievements in the commercial sector in addressing VA challenges and propose novel developments that should be on systems’ roadmaps in the coming years.