Matuszyk, Pawel

2019

Exploiting Entity Information for Stream Classification over a Stream of Reviews. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 564-573, ACM, 2019. URL

Entity-level stream classification: exploiting entity similarity to label the future observations referring to an entity. International Journal of Data Science and Analytics, 2019. URL

2018

Personalized recommender systems for product-line configuration processes. Computer Languages, Systems & Structures, 2018. URL

Entity-Level Stream Classification: Exploiting Entity Similarity to Label the Future Observations Referring to an Entity. 2018.

2017

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

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

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

2016

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

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

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

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