Design and Implementation of a Mobile Recommender System with iOS or Android
With the increase of information and online services, it becomes more and more difficult for mobile users to filter the necessary information to complete a specific task. Mobile recommender systems are widely used to help deal with this information overload by providing personalized recommendations (e.g. a shopping assistant).Although many successful web-based recommender systems exist, only few are mobile-based. Image-based critiquing and active learning can help to overcome two problems inherent to the scenario: spatial limitations in mobile interfaces and uncertainty of the user’s preferences in the beginning. The developed recommender system should allow for a more target- oriented and fruitful user experience.
The goal of this thesis is to study interaction patterns for critiquing-based mobile recommender systems. Research questions are: How can the mobile recommender support several features despite limited screen size? How does the user interact with the recommender system? Which critiquing algorithm should be applied? Based on this knowledge, a prototype should be implemented and evaluated in a user study. An application scenario might be a mobile shopping recommender system.
The candidate may choose the platform for the prototype implementation, e.g. Android or iOS. The topics can be adapted and refined for Bachelor's or Master's Thesis, or a Guided Research module in the master program for Informatics or Information Systems. Prerequisites are high motivation and good programming skills. Please send your application (brief CV and transcript of records) to Béatrice Lamche (email@example.com).
Die Studienarbeiten können natürlich auch in deutscher Sprache durchgeführt und geschrieben werden!