• 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 89 matches

• 2024
• 
  Bjørnar Vassøy, Helge Langseth:
  Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation. Artif. Intell. Rev. 57(4): 101 (2024)
• 
  Ashmi Banerjee, Paromita Banik, Wolfgang Wörndl:
  A review on individual and multistakeholder fairness in tourism recommender systems. Frontiers Big Data 6 (2024)
• 
  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. Frontiers Big Data 6 (2024)
• 
  Deepak Kumar, Tessa Grosz, Navid Rekabsaz, Elisabeth Greif, Markus Schedl:
  Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives. Frontiers Big Data 6 (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)
• 
  Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo:
  A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems. Trans. Recomm. Syst. 2(3): 19:1-19:24 (2024)
• 
  Xiaoyu Shi, Quanliang Liu, Hong Xie, Yanan Bai, Mingsheng Shang:
  Maximum Entropy Policy for Long-Term Fairness in Interactive Recommender Systems. IEEE Trans. Serv. Comput. 17(3): 1029-1043 (2024)
• 
  Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogín, Alessandro Difonzo, Dario Zanzonelli:
  Fairness in recommender systems: research landscape and future directions. User Model. User Adapt. Interact. 34(1): 59-108 (2024)
• 
  Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Meng Li, Weiming Liu, Binhui Yao, Yuyuan Li, Jianwei Yin:
  Intra- and Inter-group Optimal Transport for User-Oriented Fairness in Recommender Systems. AAAI 2024: 8463-8471
• 
  Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda:
  Comprehensive Assessment of Robustness in Fairness of GNN-based Recommender Systems against Attacks. IIR 2024: 62-65
• 
  Karlijn Dinnissen:
  Fairness and Transparency in Music Recommender Systems: Improvements for Artists. RecSys 2024: 1368-1375
• 
  Veronica Kecki, Alan Said:
  Understanding Fairness in Recommender Systems: A Healthcare Perspective. RecSys 2024: 1125-1130
• 
  Luan Soares de Souza:
  Fairness Explanations in Recommender Systems. RecSys 2024: 1353-1354
• 
  Rodrigo Souza, Marcelo Manzato:
  A Two-Stage Calibration Approach for Mitigating Bias and Fairness in Recommender Systems. SAC 2024: 1659-1661
• 
  Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Li Zhang, Yuyuan Li:
  Hypergraph Convolutional Network for User-Oriented Fairness in Recommender Systems. SIGIR 2024: 903-913
• 
  Célina Treuillier, Sylvain Castagnos, Özlem Özgöbek, Armelle Brun:
  Beyond Trade-offs: Unveiling Fairness-Constrained Diversity in News Recommender Systems. UMAP 2024: 143-148
• 
  Hyunsik Yoo, Zhichen Zeng, Jian Kang, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, Hanghang Tong:
  Ensuring User-side Fairness in Dynamic Recommender Systems. WWW 2024: 3667-3678
• 
  Yashar Deldjoo:
  Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency. CoRR abs/2401.10545 (2024)
• 
  Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo:
  A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems. CoRR abs/2402.00485 (2024)
• 
  Yashar Deldjoo:
  FairEvalLLM. A Comprehensive Framework for Benchmarking Fairness in Large Language Model Recommender Systems. CoRR abs/2405.02219 (2024)
• 
  Kirandeep Kaur, Sujit Gujar, Shweta Jain:
  Towards Fairness in Provably Communication-Efficient Federated Recommender Systems. CoRR abs/2405.15788 (2024)
• 
  Dominik Kowald:
  Transparency, Privacy, and Fairness in Recommender Systems. CoRR abs/2406.11323 (2024)
• 
  Veronica Kecki, Alan Said:
  Understanding Fairness in Recommender Systems: A Healthcare Perspective. CoRR abs/2409.03893 (2024)
• 
  Falguni Roy, Xiaofeng Ding, Kim-Kwang Raymond Choo, Pan Zhou:
  A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research. CoRR abs/2409.12651 (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)
• 
  Jesús Bobadilla, Abraham Gutiérrez:
  Generating and Testing Synthetic Datasets for Recommender Systems to Improve Fairness in Collaborative Filtering Research. AICCSA 2023: 1-6
• 
  Karlijn Dinnissen, Christine Bauer:
  How Control and Transparency for Users Could Improve Artist Fairness in Music Recommender Systems. ISMIR 2023: 482-491

skipping 59 more matches