Pawel Matuszyk

Dr.-Ing. Pawel Matuszyk

Fakultät für Informatik (FIN)
AG KMD: Wissensmanagement und Wissensentdeckung
Universitätsplatz 2, 39106, Magdeburg, Building 29, Room 124

Dr.-Ing. Pawel Matuszyk

Fakultät für Informatik (FIN)
AG KMD: Wissensmanagement und Wissensentdeckung
Universitätsplatz 2, 39106, Magdeburg, G29-124
Vita
Since 09.2017 Post-doctoral research assistant at the Knowledge Management and Discovery Lab at the Otto-von-Guericke University
04.12 - 09.17

Ph.D. student and research assistant at the Knowledge Management and Discovery Lab at the Otto-von-Guericke University

Laureate of the price for the best Ph.D. student in 2017 by the faculty of computer science at the Otto-von-Guericke University.

Finishing the Ph.D. with distinction (summa cum laude).

04.11 – 04.12

Master study of business information systems (Wirtschaftsinformatik) at the Otto-von-Guericke University

Laureate of the price for the best graduate in business information systems in year 2011/2012 (graduation within 2 semesters).

2009 – 2012 Student research assistant at the following projects: Projekt Ko-RFID, Projekt TASC, Projekt Imprint, Lehrstuhl KMD
10.07 – 03.11 Bachelor study of Business Information Systems (Wirtschaftsinformatik) at the Otto-von-Guericke University

Linkedin Profile

Personal Website

 

 Research interests:

Selected skills / techniques:

  • Recommender Systems
  • Collaborative Filtering
  • Incremental Matrix Factorization
  • Stream Mining
  • Machine Learning
  • Deep Learning
  • Incremental matrix factorization
  • Classification, regression, generative models
  • Supervised, unsupervised and semi-supervised learning
  • Predictive modelling
  • Optimization methods
  • Python, Java, R
  • Hadoop, Spark, Map-Reduce
  • Deep learning
  • Tensorflow
  • Scikit-learn
  • Weka, MOA, Rapid Miner, etc.
  • SQL

 

 

Projekte Forschungsportal

Current Projects

Completed Projects

Projects

Current Projects

Completed Projects

Publications

2017

Scalable Online Top-N Recommender Systems. In Derek Bridge, and Heiner Stuckenschmidt (Eds.), E-Commerce and Web Technologies: 17th International Conference, EC-Web 2016, Porto, Portugal, September 5-8, 2016, Revised Selected Papers, 3--20, Springer International Publishing, 2017. URL

Forgetting techniques for stream-based matrix factorization in recommender systems. Knowledge and Information Systems, Aug 4, 2017. URL

Stream-based semi-supervised learning for recommender systems. Machine Learning, 1--28, 2017. URL

2016

A Comparative Study on Hyperparameter Optimization for Recommender Systems. In Elisabeth Lex, Roman Kern, Alexander Felfernig, Kris Jack, Dominik Kowald, and Emanuel Lacic (Eds.), Workshop on Recommender Systems and Big Data Analytics (RS-BDA'16) @ iKNOW 2016, 2016. URL

A feature-based personalized recommender system for product-line configuration. In Bernd Fischer, and Ina Schaefer (Eds.), Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, GPCE 2016, 120-131, ACM, 2016. URL

2015

Semi-supervised Learning for Stream Recommender Systems. In Nathalie Japkowicz, and Stan Matwin (Eds.), Discovery Science, (9356):131-145, Springer International Publishing, 2015. URL

Forgetting Methods for Incremental Matrix Factorization in Recommender Systems. Proceedings of the 30th Annual ACM Symposium on Applied Computing, 947--953, ACM, New York, NY, USA, 2015. URL

2014

Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Users. In Vania Dimitrova, Tsvi Kuflik, David Chin, Francesco Ricci, Peter Dolog, and Geert-Jan Houben (Eds.), User Modeling, Adaptation, and Personalization, (8538):146–157, Springer International Publishing, 2014. URL

Predicting the Performance of Collaborative Filtering Algorithms. Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14), 38:1--38:6, ACM, New York, NY, USA, 2014. URL

Selective Forgetting for Incremental Matrix Factorization in Recommender Systems. In Sašo Džeroski, Panče Panov, Dragi Kocev, and Ljupčo Todorovski (Eds.), Discovery Science, (8777):204-215, Springer International Publishing, 2014. URL

2013

Correcting the Usage of the Hoeffding Inequality in Stream Mining. In Allan Tucker, Frank Höppner, Arno Siebes, and Stephen Swift (Eds.), Advances in Intelligent Data Analysis XII, (8207):298-309, Springer Berlin Heidelberg, 2013. URL

Framework for Storing and Processing Relational Entities in Stream Mining. In Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, and Guandong Xu (Eds.), Advances in Knowledge Discovery and Data Mining, (7819):497-508, Springer Berlin Heidelberg, 2013. URL

2012

Framework for Computer Aided Analysis of Medical Protocols in a Hospital.. In Emmanuel Conchon, Carlos Manuel B. A. Correia, Ana L. N. Fred, and Hugo Gamboa (Eds.), HEALTHINF, 225-230, SciTePress, 2012. URL

2011

Prediction of surgery duration using empirical anesthesia protocols. The first International Workshop on Knowledge Discovery in Health Care and Medicine. - Athen, 2011. URL

 

Last Modification: 27.10.2017 - Contact Person:

Sie können eine Nachricht versenden an: M. Sc. Pawel Matuszyk
Sicherheitsabfrage:
Captcha
 
Lösung: