Search Results - "Burke, Robin"

Preface to the special issue on fair, accountable, and transparent recommender systems.

Authors: Burke, Robin Ekstrand, Michael D. Tintarev, Nava et al.

Source: User Modeling & User-Adapted Interaction; Jul2021, Vol. 31 Issue 3, p371-375, 5p

Impact of variable titer COVID-19 convalescent plasma and recipient SARS-CoV2-specific humoral immunity on survival in hospitalized patients.

Authors: Iasella, Carlo J. Hannan, Stefanie J. Lyons, Emily J. et al.

Source: PLoS ONE. 10/24/2024, Vol. 19 Issue 10, p1-11. 11p.

RecSOI: recommending research directions using statements of ignorance.

Authors: Bibal, Adrien Salem, Nourah M. Cardon, Rémi et al.

Source: Journal of Biomedical Semantics. 4/22/2024, Vol. 15 Issue 1, p1-19. 19p.

Multistakeholder recommendation: Survey and research directions.

Authors: Abdollahpouri, Himan Adomavicius, Gediminas Burke, Robin et al.

Source: User Modeling & User-Adapted Interaction; Mar2020, Vol. 30 Issue 1, p127-158, 32p

The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform.

Authors: Liu, Jia Cong, Ziwei

Source: Journal of Marketing Research (JMR). Aug2023, Vol. 60 Issue 4, p767-791. 25p. 3 Diagrams, 8 Charts, 9 Graphs.

Reading When Stressed: Understanding Motivations for Reading Fiction and Enhancing Future Recommendation Services.

Authors: Cho, Hyerim Lee, Wan-Chen Huang, Li-Min et al.

Source: Library Quarterly. Jul2024, Vol. 94 Issue 3, p253-271. 19p.

Recommender Systems: An Overview.

Authors: Burke, Robin Felfernig, Alexander Göker, Mehmet H.

Source: AI Magazine; Fall2011, Vol. 32 Issue 3, p13-18, 6p

Personalized Recommendation of PoIs to People with Autism.

Authors: Burke, Robin¹

Source: Communications of the ACM. Feb2022, Vol. 65 Issue 2, p100-100. 1p.

A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems.

Authors: MANSOURY, MASOUD ABDOLLAHPOURI, HIMAN PECHENIZKIY, MYKOLA et al.

Source: ACM Transactions on Information Systems; 2022, Vol. 40 Issue 2, p1-31, 31p

Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model.

Authors: van der Lee, Chris Ferreira, Thiago Castro Emmery, Chris et al.

Source: Computational Linguistics; Sep2023, Vol. 49 Issue 3, p555-611, 57p

Fairness in Information Access Systems.

Authors: Ekstrand, Michael D. Das, Anubrata Burke, Robin et al.

Source: Foundations & Trends in Information Retrieval; 2022, Vol. 16 Issue 1/2, p1-177, 177p

Recommendation in the Social Web.

Authors: Burke, Robin Gemmell, Jonathan Hotho, Andreas

Source: AI Magazine; Fall2011, Vol. 32 Issue 3, p46-56, 11p

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De-centering the (Traditional) user: Multistakeholder evaluation of recommender systems.

Authors: Burke, Robin Adomavicius, Gediminas Bogers, Toine et al.

Source: International Journal of Human-Computer Studies; Sep2025, Vol. 203, pN.PAG-N.PAG, 1p

A COMPARATIVE STUDY OF FAIRNESS IN AI-ENABLED AND LLM-BASED RECOMMENDATION SYSTEMS.

Authors: Jiaqian Zhang Jianshan Sun Ying Xue et al.

Source: Journal of Electronic Commerce Research; 2025, Vol. 26 Issue 3, p237-265, 29p

Cutting Through the Online Review Jungle — Investigating Selective eWOM Processing.

Authors: Gottschalk, Sabrina A. Mafael, Alexander

Source: Journal of Interactive Marketing. Feb2017, Vol. 37, p89-104. 16p.

Attacks and Remedies in Collaborative Recommendation.

Authors: Mobasher, Bamshad Burke, Robin Bhaumik, Runa

Source: IEEE Intelligent Systems; May/June 2007, Vol. 22 Issue 3, p56-63, 8p

Powerlessness and Personalization.

Authors: Burke, Victoria I. Burke, Robin D.

Source: International Journal of Applied Philosophy; Fall2019, Vol. 33 Issue 2, p319-343, 25p

Beyond the Click: AI-Powered UX Strategies Driving Consumer Confidence and Conversion.

Authors: H. K., Prasad Kalhapure, Balasaheb Mohanapriya, T. et al.

Source: Advances in Consumer Research. 2025, Vol. 2 Issue 4, p3787-3793. 7p.

Shilling attacks against collaborative recommender systems: a review.

Authors: Si, Mingdan Li, Qingshan

Source: Artificial Intelligence Review; Jan2020, Vol. 53 Issue 1, p291-319, 29p