• 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 63 matches

• 2024
• 
  Yao Wu, Jian Cao, Guandong Xu:
  Fairness in Recommender Systems: Evaluation Approaches and Assurance Strategies. ACM Trans. Knowl. Discov. Data 18(1): 10:1-10:37 (2024)
• 2023
• 
  Virginie Do:
  Fairness in recommender systems: insights from social choice. (Equité des systèmes de recommandation: les perspectives du choix social). PSL University, Paris, France, 2023
• 
  Meike Zehlike, Ke Yang, Julia Stoyanovich:
  Fairness in Ranking, Part II: Learning-to-Rank and Recommender Systems. ACM Comput. Surv. 55(6): 117:1-117:41 (2023)
• 
  Nikzad Chizari, Keywan Tajfar, María N. Moreno García:
  Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems. Inf. 14(2): 131 (2023)
• 
  Enrique Amigó, Yashar Deldjoo, Stefano Mizzaro, Alejandro Bellogín:
  A unifying and general account of fairness measurement in recommender systems. Inf. Process. Manag. 60(1): 103115 (2023)
• 
  Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Weiming Liu, Jun Wang, Wenjie Cheng, Yuyuan Li:
  In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems. ACM Multimedia 2023: 6190-6201
• 
  Alexandra Vultureanu-Albisi, Costin Badica:
  The Significance of Enhancing Data Fairness in Educational Recommender Systems. RoEduNet 2023: 1-6
• 
  Ashmi Banerjee:
  Fairness and Sustainability in Multistakeholder Tourism Recommender Systems. UMAP 2023: 274-279
• 
  Paromita Banik, Ashmi Banerjee, Wolfgang Wörndl:
  Understanding User Perspectives on Sustainability and Fairness in Tourism Recommender Systems. UMAP (Adjunct Publication) 2023: 241-248
• 
  Lequn Wang, Thorsten Joachims:
  Uncertainty Quantification for Fairness in Two-Stage Recommender Systems. WSDM 2023: 940-948
• 
  Bjørnar Vassøy, Helge Langseth:
  Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation. CoRR abs/2305.09330 (2023)
• 
  Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu C. Aggarwal, Tyler Derr:
  Fairness and Diversity in Recommender Systems: A Survey. CoRR abs/2307.04644 (2023)
• 
  Hyunsik Yoo, Zhichen Zeng, Jian Kang, Zhining Liu, David Zhou, Fei Wang, Eunice Chan, Hanghang Tong:
  Ensuring User-side Fairness in Dynamic Recommender Systems. CoRR abs/2308.15651 (2023)
• 
  Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Weiming Liu, Jun Wang, Wenjie Cheng, Yuyuan Li:
  In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems. CoRR abs/2309.01335 (2023)
• 
  Ashmi Banerjee, Paromita Banik, Wolfgang Wörndl:
  Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems. CoRR abs/2309.02052 (2023)
• 
  Saeedeh Karimi, Hossein A. Rahmani, Mohammadmehdi Naghiaei, Leila Safari:
  Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems. CoRR abs/2309.04250 (2023)
• 
  Amifa Raj, Michael D. Ekstrand:
  Towards Measuring Fairness in Grid Layout in Recommender Systems. CoRR abs/2309.10271 (2023)
• 
  Tomislav Duricic, Dominik Kowald, Emanuel Lacic, Elisabeth Lex:
  Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks. CoRR abs/2310.02294 (2023)
• 2022
• 
  Nasim Sonboli, Robin Burke, Michael D. Ekstrand, Rishabh Mehrotra:
  The Multisided Complexity of Fairness in Recommender Systems. AI Mag. 43(2): 164-176 (2022)
• 
  Yangkun Li, Mohamed-Laid Hedia, Weizhi Ma, Hongyu Lu, Min Zhang, Yiqun Liu, Shaoping Ma:
  Contextualized Fairness for Recommender Systems in Premium Scenarios. Big Data Res. 27: 100300 (2022)
• 
  Karlijn Dinnissen, Christine Bauer:
  Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review. Frontiers Big Data 5 (2022)
• 
  Elizabeth Gómez, Carlos Shui Zhang, Ludovico Boratto, Maria Salamó, Guilherme Ramos:
  Enabling cross-continent provider fairness in educational recommender systems. Future Gener. Comput. Syst. 127: 435-447 (2022)
• 
  Elizabeth Gómez, Ludovico Boratto, Maria Salamó:
  Provider fairness across continents in collaborative recommender systems. Inf. Process. Manag. 59(1): 102719 (2022)
• 
  Rodrigo Borges, Kostas Stefanidis:
  Feature-blind fairness in collaborative filtering recommender systems. Knowl. Inf. Syst. 64(4): 943-962 (2022)
• 
  Mohammadmehdi Naghiaei, Hossein A. Rahmani, Yashar Deldjoo:
  PyCPFair: A framework for consumer and producer fairness in recommender systems. Softw. Impacts 13: 100382 (2022)
• 
  Nil-Jana Akpinar, Cyrus DiCiccio, Preetam Nandy, Kinjal Basu:
  Long-term Dynamics of Fairness Intervention in Connection Recommender Systems. AIES 2022: 22-35
• 
  Ludovico Boratto, Gianni Fenu, Mirko Marras, Giacomo Medda:
  Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations. ECIR (1) 2022: 552-566
• 
  Preetam Nandy, Cyrus DiCiccio, Divya Venugopalan, Heloise Logan, Kinjal Basu, Noureddine El Karoui:
  Achieving Fairness via Post-Processing in Web-Scale Recommender Systems✱. FAccT 2022: 715-725
• 
  Adam Mehdi Arafan, David Graus, Fernando P. Santos, Emma Beauxis-Aussalet:
  End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates. HR@RecSys 2022
• 
  Pratyush Yadav, Nargis Pervin:
  Antecedents of Perceived Fairness and User Trust in Scientific Recommender Systems. PACIS 2022: 89

skipping 33 more matches