Title:
Generic Recommender System

Description:
In a world of information overload, recommender systems filter relevant information and provide personalized content recommendations to users based on their personal background, preferences and interests. Numerous recommendation methods were designed over the years to enhance the preciseness of recommendations, such as content-based and collaborative filtering or hybrid approaches. However, these recommender systems are often focused on a certain application using certain recommendation algorithms and filters without the ability to adapt the system to another application or to enhance it by new functions.

The main objective of this thesis is to develop a generic recommender system that can provide recommendations for different types of applications, especially mobile services. The system should also be extendable in a plug-in-manner in order to add new recommendation algorithms, filters or databases fulfilling the requirements of modern mobile services.

Challenges:

• Strong practical relevance

Prerequisites:

• Proficiency in software development
• Interest to work in the field of Recommender Systems

Supervisor: Prof. Dr. Axel Küpper, Abdulbaki Uzun

Duration: 6 months