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Veranstaltungen im Sommersemester 2023 / Master-Seminar: Data Analytics

Online Social Networks are a popular way for people to interact, communicate, express themselves, and share contents. These platforms provide a rich ground for various machine learning applications that can analyze user behavior and interac- tions for recommender systems, link prediction, location prediction, event detection, and sentiment analysis. The aim of this seminar is to expose the students to the applications of machine learning in the domain of social networks. It will enhance the students’ abilities to comprehend, explain and criticize state-of-the-art research. On the other hand, it serves as a playground for developing analytical thinking.

Seminar Topic Recommender Systems, List of papers:
  1. Matrix factorization techniques for recommender systems.
  2. Deep matrix factorization models for recommender systems.
  3. Collaborative Filtering for Implicit Feedback Datasets.
  4. Collaborative Filtering with Temporal Dynamics.
  5. Contextual Collaborative Filtering via Hierarchical Matrix Factorization.
  6. Neural Collaborative Filtering.
  7. Factorization Machines with libFM.
  8. Translation-based Factorization Machines for Sequential Recommendation.
  9. Deep Neural Networks for YouTube Recommendations.
  10. Recurrent recommender networks.
  11. Neural Collaborative Filtering vs. Matrix Factorization Revisited.
  12. BPR: Bayesian Personalized Ranking from Implicit Feedback.
  13. VBPR: visual bayesian personalized ranking from implicit feedback.
  14. Visually-Aware Fashion Recommendation and Design with Generative Image Models.
  15. Personalized top-n sequential recommendation via convolutional sequence embedding.
Instructor: Lukas Brinkmeyer
Zeit: Di 14-16
Beginn: 11.04.2023
Zuordnung:MSc WI & IMIT & DA
Modul- Handbuch:MHB
Voheriger Durchlauf: here