Ladislav Peška, M.Sc.

peska<at>ksi.mff.cuni.cz
assistant professor

Research topics

  • Recommender systems
  • User preferences

Teachingtop

CodeTitleLinks
NDBI021Customer preferences
NPRG045Individual Software Project
NPRG065Programming in Python
NSWI122Dissertation Thesis Preparation
NSWI166Introduction to Recommender Systems
NSWI167Advanced Methods for Recommender Systems

Supervised thesestop

TitleStudentTypeStartedDefended
Books Recommender System via Linked Open DataLadislav MalečekBS20162017
Recommender systems for culture eventsZuzana VytiskováMS20152017
Content-based recommender systemsMaria MichalkoMS20132015

Publicationstop

2017

Refereed (journals/proceedings) (8)

  • Buza K., Peška L.: ALADIN: A New Approach for Drug--Target Interaction Prediction, accepted for publication in THE EUROPEAN CONFERENCE ON MACHINE LEARNING & PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2017, SKOPJE, MACEDONIA, 2017
  • [Journal (without IF)]Buza K., Peška L.: Drug-target interaction prediction with Bipartite Local Models and hubness-aware regression, in Neurocomputing, Vol. 260, Num. 10, ISSN: 0925-2312, pp. 284-293, 2017 - text
  • Peška L.: Linking Content Information with Bayesian Personalized Ranking via Multiple Content Alignments, in Proceedings of the 28th ACM Conference on Hypertext and Social Media, Prague, Czech Republic, ACM, ISBN: 978-1-4503-4708-2, pp. 175-183, 2017 - text
  • Peška L.: Multimodal Implicit Feedback for Recommender Systems, in Proceedings of the 17th Conference on Information Technologies - Applications and Theory (ITAT 2017), Martinske Hole, CEUR-WS, ISSN: 1613-0073, pp. 240-245, 2017 - text
  • [Journal (without IF)]Peška L., Krisztian B., Julia K.: Drug-target interaction prediction: A Bayesian ranking approach, in Computer Methods and Programs in Biomedicine, Vol. 152, Num. 12, ISSN: 0169-2607, pp. 15-21, 2017 - text
  • Peška L., Trojanová H.: Towards Recommender Systems for Police Photo Lineup, in Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, Como, Italy, Association for Computing Madinery, ISBN: 978-1-4503-5353-3, pp. 19-23, 2017 - text
  • Peška L., Vojtáš P.: Towards Complex User Feedback and Presentation Context in Recommender Systems, in Datenbanksysteme für Business, Technologie und Web (BTW 2017), 17. Fachtagung des GI-Fachbereichs „Datenbanken und Informationssysteme&quot; (DBIS), 6.-10. März 2017, Stuttgart, Germany, Workshopband, Stuttgart, Germany, Gesellschaft für Informatik e.V., ISBN: 978-3-88579-660-2, pp. 127-134, 2017 - text
  • [Journal (without IF)]Peška L., Vojtáš P.: Using Implicit Preference Relations to Improve Recommender Systems, in Journal on Data Semantics, Vol. 6, Num. 1, ISSN: 1861-2032, pp. 15-30, 2017 - text

2016

Refereed (journals/proceedings) (3)

  • Meszlényi R., Peška L., Gál V., Vidnyánszky Z., Buza K.: A model for classification based on the functional connectivity pattern dynamics of the brain, in 2016 Third European Network Intelligence Conference (ENIC), Wrocław, Poland, IEEE, ISBN: 978-1-5090-3455-0, pp. 203-208, 2016 - text
  • Meszlényi R., Peška L., Gál V., Vidnyánszky Z., Buza K.: Classification of fMRI data using Dynamic Time Warping based functional connectivity analysis, in 24th European Signal Processing Conference, EUSIPCO 2016, Budapest, Hungary, IEEE, ISBN: 978-0-9928626-5-7, ISSN: 2219-5491, pp. 245-249, 2016 - text
  • Peška L.: Using the Context of User Feedback in Recommender Systems, in ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE, Telč, Česká Republika, Open Publishing Association, ISSN: 2075-2180, pp. 1-12, 2016 - text

2015

Refereed (journals/proceedings) (5)

  • Kopecký M., Peška L., Vojtáš P., Vomlelová M.: Monotonization of User Preferences, in Proceedings of the 11th International Conference FQAS 2015, Krakov, Springer, ISBN: 978-3-319-26154-6, ISSN: 2194-5357, pp. 29-40, 2015
  • Peška L., Vojtáš P.: Biased k-NN Similarity Content Based Prediction of Movie Tweets Popularity, in Proceedings of the Dateso 2015 Annual International Workshop on DAtabases, TExts, Specifications and Objects, Neprivec u Sobotky, Jicin, Czech Republic, CEUR-WS.org, ISSN: 1613-0073, pp. 101-110, 2015 - text
  • Peška L., Vojtáš P.: How to Interpret Implicit User Feedback?, in RecSys 2015 Poster Proceeding, Vienna, Austria, CEUR-WS.org, ISSN: 1613-0073, pp. nestránkováno, 2015 - text
  • Peška L., Vojtáš P.: Using Implicit Preference Relations to Improve Content Based Recommending, in E-Commerce and Web Technologies, 16th International Conference on Electronic Commerce and Web Technologies, EC-Web 2015, Valencia, Spain, September 2015, Revised Selected Papers, Valencia, Spain, Springer, ISBN: 978-3-319-27728-8, ISSN: 1865-1348, pp. 3-16, 2015 - text
  • Peška L., Vojtáš P.: Using Linked Open Data in Recommender Systems, in Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics, Limassol, Cyprus, ACM, ISBN: 978-1-4503-3293-4, pp. 17_1-17_6, 2015 - text

2014

Refereed (journals/proceedings) (6)

  • Peška L.: IPIget: The Component for Collecting Implicit User Preference Indicators, in Proceedings of the 14th conference Information Technologies - Applications and Theory (ITAT 2014) - Workshops and Posters, Demänovská Dolina, Slovakia, Ústav informatiky AV ČR, ISBN: 978-80-87136-19-5, pp. 22-26, 2014
  • Peška L., Vojtáš P.: Hybrid Recommending Exploiting Multiple DBPedia Language Editions, in Semantic Web Evaluation Challenge, Anissaras, Crete, Greece, Springer Verlag, ISBN: 978-3-319-12023-2, ISSN: 1865-0929, pp. 144-149, 2014
  • Peška L., Vojtáš P.: Interpreting Web Shop User's Behavioral Patterns as Fictitious Explicit Rating for Preference Learning, in Rules on the Web. From Theory to Applications, Prague, Czech Republic, Springer, ISBN: 978-3-319-09869-2, ISSN: 0302-9743, pp. 251-265, 2014
  • Peška L., Vojtáš P.: Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations, in E-Commerce and Web Technologies, Munich, Germany, Springer, ISBN: 978-3-319-10490-4, ISSN: 1865-1348, pp. 138-145, 2014
  • Peška L., Vojtáš P.: Recommending for Disloyal Customers with Low Consumption Rate, in SOFSEM 2014: Theory and Practice of Computer Science, Nový Smokovec, Slovakia, Springer, ISBN: 978-3-319-04297-8, ISSN: 0302-9743, pp. 455-465, 2014
  • Vojtáš P., Peška L.: e-Shop User Preferences via User Behavior, in ICE-B 2014 - Proceedings of the 11th International Conference on e-Business, Vienna, Austria, 28-30 August, 2014, Vienna, Austria, SciTePress, ISBN: 978-989-758-043-7, pp. 68-75, 2014

2013

Refereed (journals/proceedings) (7)

  • Kopecký M., Pokorný J., Vojtáš P., Kubalík J., Matoušek K., Maryška M., Novotný O., Peška L.: Testing and Evaluating Software in a Social Network Creating Baseline Knowledge, in Frontiers in Artificial Intelligence and Applications, Vol. neuveden, Num. 251, ISSN: 0922-6389, pp. 127-141, 2013
  • Peška L.: How Far Ahead Can Recommender Systems Predict?, in ITAT 2013: Information Technologies—Applications and Theory (Proceedings), Donovaly, Slovakia, CreateSpace Independent Publishing Platform, ISBN: 978-1-4909-5208-6, pp. 22-26, 2013
  • [Journal (without IF)]Peška L., Lašek I., Eckhardt A., Dědek J., Vojtáš P., Dominik F.: Towards web semantization and user understanding, in Frontiers in Artificial Intelligence and Applications, Vol. 251, Num. January, ISSN: 0922-6389, pp. 63-81, 2013
  • Peška L., Vojtáš P.: Enhancing Recommender System with Linked Open Data, in Lecture Notes in Computer Science, Granada, Spain, Springer Berlin / Heidelberg, ISBN: 978-3-642-40768-0, ISSN: 0302-9743, pp. 483-494, 2013
  • Peška L., Vojtáš P.: Estimating importance of implicit factors in E-commerce recommender systems, in Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, Craiova, Romania, ACM, ISBN: 978-1-4503-0915-8, pp. 1-4, 2013
  • Peška L., Vojtáš P.: Evaluating various implicit factors in e-commerce, accepted for publication in CEUR Workshop Proceedings, Dublin, Ireland, Ceur-WS.org, ISSN: 1613-0073, pp. 51-55, 2013
  • Peška L., Vojtáš P.: Negative implicit feedback in e-commerce recommender systems, in Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, Madrid, Spain, ACM, ISBN: 1-4503-1850-9, pp. 451-454, 2013

Book chapters (1)

  • Nečaský M., Fišer D., Lašek I., Peška L., Vojtáš P.: User Assisted Creation of Open-Linked Data for Training Web Information Extraction in a Social Network, in Cases On Open-Linked Data and Semantic Web Applications, IGI Global, ISBN: 978-1-4666-2827-4, pp. 28-38, 2013

2012

Refereed (journals/proceedings) (1)

  • Peška L.: User Feedback and Preferences Mining, in Lecture Notes in Computer Science, Vol. neuveden, Num. 7379, ISSN: 0302-9743, pp. 382-386, 2012

2011

Refereed (journals/proceedings) (1)

  • Peška L., Eckhardt A., Vojtáš P.: UPComp - A PHP Component for Recommendation Based on User Behaviour, in Proceedings of WI-IAT 2011, Lyon, France, IEEE Computer Society, ISBN: 978-1-4577-1373-6, pp. 306-309, 2011

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