Fraunhofer Institute for Open Communication Systems
Fraunhofer Institute for Open Communication Systems

Software Campus

Shot of a group of businesspeople having a meeting in a modern office
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About Software Campus

The executive development program is made for Masters' and doctoral students in computer science or computer-science related disciplines who want to establish their own business or take over executive management functions. Christopher Krauss, Software Campus participant of Fraunhofer FOKUS, researches on “Machine Learning and Semantic Analyses to improve Context-aware Time-dependent Data Mining Algorithms”. The doctoral project is substantially supported by the Holtzbrinck Publishing Group.

Abstract

The processing of Big Data in modern internet services quite often faces a lack of describing information of its users and the presented services and products. However, the prognosis of the user's behavior through Data Mining Algorithms should be viewed as crucial. The missing data must be captured via contextual analyses, abstracted as time-dependent data and finally optimized for the usage of recommendation algorithms.

In order to improve web services data is being collected from the presented services and products as well as from the users and their way of conduct. The gained information can be used among others for market analyses, for the improvement and optimization of the offer and the products as well as for the personalization and individualization of the services which are provided for the respective user. The main area of application of this project is the improvement of Recommendation Engines which will provide the personalized recommendation for users and which will ensure a more effective and shorter decision process for the customer.

Objectives of the project

Known issues of recommendation algorithms are a result of the so called “Cold Start Problem”. This issue is caused by a lack of sufficient data of users, items or the content, which are essential for the calculation of context-sensitive influences. Along with this comes the “Sparsity Problem” which also exposes the problem of recommendation systems which are being provided with too little information of user feedback such as likes, evaluations and views. As a consequent collaborative and knowledge-based filtering algorithms are unable of precise prediction which is causing a decline of the customer satisfaction. If beyond that there also is a lack of metadata, the calculation of similarities through content-based filtering algorithms is likely to fail as well. Most of the needed data that exists in the World Wide Web, however, is mostly stored unstructured and without time- and context-dependency. The objective of this project is the extension of recommendation algorithms by context and time-dependent user-item-data from the restart to the permanent operation of the system. For this it is necessary to gain descriptive data which shall improve the comparability of users and items over time, and to collect and train collaborative data for various context-factors.

Fraunhofer FOKUS Role

Fraunhofer FOKUS Competence Center Future Applications and Media (FAME) contributes as an academic partner to the Software Campus program. The FAME project manager leads a team of scientific assistants and students to solve the academic research question in cooperation with companies of the Holtzbrinck Publishing Group.