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.

Project details

Duration: March 2015 to February 2017

This project has received funding from the German Federal Ministry of Education and Research (BMBF) under grant agreement No 01|S12053.