• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 126 matches

• 2024
• 
  Stepán Balcar, Ladislav Peska, Peter Vojtás:
  Personalisation of d'Hondt's algorithm and its use in recommender ecosystems. CoRR abs/2406.07264 (2024)
• 2022
• 
  Stepán Balcar, Ladislav Peska, Peter Vojtás:
  Hierarchical Portfolios in Recommender Ecosystems: Multi-level aggregations of heterogeneous ensembles integrating long-term and short-term motivations. ICCAI 2022: 280-285
• 2021
• 
  Samuel Brezani, R. Hrasko, D. Vanco, J. Vojtas, Peter Vojtás:
  Deep Learning for Knowledge Extraction from UAV Images. EJC 2021: 44-63
• 
  Samuel Brezani, Peter Vojtás:
  Aggregation for Flexible Challenge Response. FQAS 2021: 209-222
• 
  Samuel Brezani, R. Hrasko, D. Vanco, Peter Vojtás:
  Increasing Business Opportunities for Drone Services. ICE-B 2021: 93-100
• 
  Samuel Brezani, R. Hrasko, Peter Vojtás:
  Smart extensions to regular cameras in the industrial environment. ISM 2021: 298-307
• 2020
• 
  Michal Kopecky, Peter Vojtás:
  Visual e-commerce values filtering framework with spatial database metric. Comput. Sci. Inf. Syst. 17(3): 983-1006 (2020)
• 
  Ladislav Peska, Peter Vojtás:
  Predictability of Off-line to On-line Recommender Measures via Scaled Fuzzy Implicators. FUZZ-IEEE 2020: 1-8
• 
  Ladislav Peska, Peter Vojtás:
  Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce. HT 2020: 291-300
• 2019
• 
  Michal Kopecky, Peter Vojtás:
  Graphical E-Commerce Values Filtering Model in Spatial Database Framework. ADBIS (Short Papers and Workshops) 2019: 210-220
• 2018
• 
  Francesco Bonchi, Peter Dolog, Denis Helic, Peter Vojtás:
  Invited papers from the ACM conference on hypertext and social media. New Rev. Hypermedia Multim. 24(2): 61-62 (2018)
• 
  Pavel Vojtas, Jan Stepán, David Sec, Richard Cimler, Ondrej Krejcar:
  Voice Recognition Software on Embedded Devices. ACIIDS (1) 2018: 642-650
• 
  Ladislav Peska, Peter Vojtás:
  Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce. CoRR abs/1809.03186 (2018)
• 2017
• 
  Ladislav Peska, Peter Vojtás:
  Using Implicit Preference Relations to Improve Recommender Systems. J. Data Semant. 6(1): 15-30 (2017)
• 
  Peter Dolog, Peter Vojtás, Francesco Bonchi, Denis Helic:
  28th ACM International Conference on Hypertext and Social Media. SIGWEB Newsl. 2017(Winter): 2:1-2:8 (2017)
• 
  Ladislav Peska, Peter Vojtás:
  Towards Complex User Feedback and Presentation Context in Recommender Systems. BTW (Workshops) 2017: 203-207
• 
  Michal Kopecky, Marta Vomlelová, Peter Vojtás:
  Repeatable Web Data Extraction and Interlinking. ITAT 2017: 228-234
• 
  Peter Dolog, Peter Vojtás, Francesco Bonchi, Denis Helic:
  Proceedings of the 28th ACM Conference on Hypertext and Social Media, HT 2017, Prague, Czech Republic, July 4-7, 2017. ACM 2017, ISBN 978-1-4503-4708-2 [contents]
• 2016
• 
  Michal Kopecky, Marta Vomlelová, Peter Vojtás:
  Basis Functions as Pivots in Space of Users Preferences. ADBIS (Short Papers and Workshops) 2016: 45-53
• 2015
• 
  Ladislav Peska, Peter Vojtás:
  Biased k-NN Similarity Content Based Prediction of Movie Tweets Popularity. DATESO 2015: 101-110
• 
  Ladislav Peska, Peter Vojtás:
  Using Implicit Preference Relations to Improve Content Based Recommending. EC-Web 2015: 3-16
• 
  Michal Kopecky, Ladislav Peska, Peter Vojtás, Marta Vomlelová:
  Monotonization of User Preferences. FQAS 2015: 29-40
• 
  Peter Vojtás, Michal Kopecky, Marta Vomlelová:
  Understanding Transparent and Complicated Users as Instances of Preference Learning for Recommender Systems. MEMICS 2015: 23-34
• 
  Ladislav Peska, Peter Vojtás:
  How to Interpret Implicit User Feedback? RecSys Posters 2015
• 
  Marta Vomlelová, Michal Kopecky, Peter Vojtás:
  Transformation and Aggregation Preprocessing for Top-k Recommendation GAP Rules Induction. Challenge+DC@RuleML 2015
• 
  Ladislav Peska, Peter Vojtás:
  Using Linked Open Data in Recommender Systems. WIMS 2015: 17:1-17:6
• 2014
• 
  Ladislav Peska, Peter Vojtás:
  Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations. EC-Web 2014: 138-145
• 
  Ladislav Peska, Peter Vojtás:
  Hybrid Recommending Exploiting Multiple DBPedia Language Editions. SemWebEval@ESWC 2014: 144-149
• 
  Peter Vojtás, Ladislav Peska:
  e-Shop User Preferences via User Behavior. ICE-B 2014: 68-75
• 
  Ladislav Peska, Peter Vojtás:
  Interpreting Web Shop User's Behavioral Patterns as Fictitious Explicit Rating for Preference Learning. RuleML 2014: 251-265

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