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ORCIDRobin D. Burke
Robin Burke
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- affiliation: University of Colorado, Boulder, CO, USA
- affiliation (former): DePaul University, Chicago, IL, USA
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2020 – today
- 2024
[j28]Adrien Bibal, Nourah M. Salem, Rémi Cardon, Elizabeth K. White, Daniel E. Acuna, Robin Burke, Lawrence E. Hunter:
RecSOI: recommending research directions using statements of ignorance. J. Biomed. Semant. 15(1): 2 (2024)- [c123]Jessie J. Smith
, Aishwarya Satwani, Robin Burke, Casey Fiesler:
Recommend Me? Designing Fairness Metrics with Providers. FAccT 2024: 2389-2399 - 2023
- [c122]Jessie J. Smith, Anas Buhayh, Anushka Kathait, Pradeep Ragothaman, Nicholas Mattei, Robin Burke, Amy Voida:
The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation. FAccT 2023: 1652-1663 - [c121]Nihal Alaqabawy, Aishwarya Satwani, Amy Voida, Robin Burke:
"It's just a robot that looks at numbers": Restoring Journalistic Voice in News Recommendation. INRA@RecSys 2023 - [c120]Bamshad Mobasher, Styliani Kleanthous, Jahna Otterbacher, Robin Burke, Avital Shulner-Tal:
6th Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP 2023). UMAP (Adjunct Publication) 2023: 239-240 - [i35]Amanda Aird, Paresha Farastu, Joshua Sun, Amy Voida, Nicholas Mattei, Robin Burke:
Dynamic fairness-aware recommendation through multi-agent social choice. CoRR abs/2303.00968 (2023) - [i34]Amanda Aird, Cassidy All, Paresha Farastu, Elena Stefancova, Joshua Sun, Nicholas Mattei, Robin Burke:
Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF. CoRR abs/2309.08621 (2023) - 2022
- [j27]Nasim Sonboli, Robin Burke, Michael D. Ekstrand, Rishabh Mehrotra:
The Multisided Complexity of Fairness in Recommender Systems. AI Mag. 43(2): 164-176 (2022) - [j26]Robin Burke:
Personalized recommendation of PoIs to people with autism: technical perspective. Commun. ACM 65(2): 100 (2022) - [j25]Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz:
Fairness in Information Access Systems. Found. Trends Inf. Retr. 16(1-2): 1-177 (2022) - [j24]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems. ACM Trans. Inf. Syst. 40(2): 32:1-32:31 (2022) - [c119]Jessie J. Smith, Lucia Jayne, Robin Burke:
Recommender Systems and Algorithmic Hate. RecSys 2022: 592-597 - [c118]Nasim Sonboli, Toshihiro Kamishima, Amifa Raj, Luca Belli, Robin Burke:
FAccTRec 2022: The 5th Workshop on Responsible Recommendation. RecSys 2022: 686-687 - [c117]Styliani Kleanthous, Bamshad Mobasher, Tsvika Kuflik, Bettina Berendt, Robin Burke, Jahna Otterbacher, Nasim Sonboli, Avital Shulner-Tal:
5th Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP 2022). UMAP (Adjunct Publication) 2022: 209-210 - [c116]Robin Burke, Nicholas Mattei, Vladislav Grozin, Amy Voida, Nasim Sonboli:
Multi-agent Social Choice for Dynamic Fairness-aware Recommendation. UMAP (Adjunct Publication) 2022: 234-244 - [p4]Robin Burke:
Personalization, Fairness, and Post-Userism. Perspectives on Digital Humanism 2022: 145-150 - [r4]Himan Abdollahpouri, Robin Burke:
Multistakeholder Recommender Systems. Recommender Systems Handbook 2022: 647-677 - [r3]Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz:
Fairness in Recommender Systems. Recommender Systems Handbook 2022: 679-707 - [i33]Jessie J. Smith, Lucia Jayne, Robin Burke:
Recommender Systems and Algorithmic Hate. CoRR abs/2209.02159 (2022) - [i32]Paresha Farastu, Nicholas Mattei, Robin Burke:
Who Pays? Personalization, Bossiness and the Cost of Fairness. CoRR abs/2209.04043 (2022) - 2021
- [j23]Robin Burke, Cristina Gena, Tsvi Kuflik, Ilaria Torre:
Virtual ACM UMAP 2020: the 28th conference on user modeling, adaptation, and personalization. SIGWEB Newsl. 2021(Winter): 1:1-1:4 (2021) - [j22]Masoud Mansoury, Robin Burke, Bamshad Mobasher:
Flatter Is Better: Percentile Transformations for Recommender Systems. ACM Trans. Intell. Syst. Technol. 12(2): 19:1-19:16 (2021) - [j21]Robin Burke, Michael D. Ekstrand, Nava Tintarev, Julita Vassileva:
Preface to the special issue on fair, accountable, and transparent recommender systems. User Model. User Adapt. Interact. 31(3): 371-375 (2021) - [c115]Nasim Sonboli, Masoud Mansoury, Ziyue Guo, Shreyas Kadekodi, Weiwen Liu, Zijun Liu, Andrew Schwartz, Robin Burke:
librec-auto: A Tool for Recommender Systems Experimentation. CIKM 2021: 4584-4593 - [c114]Ian Burke, Robin Burke, Goran Kuljanin:
Fair Candidate Ranking with Spatial Partitioning: Lessons from the SIOP ML competition. HR@RecSys 2021 - [c113]Joseph A. Konstan, Robin Burke, Edward C. Malthouse:
Towards an Experimental News User Community as Infrastructure for Recommendation Research (abstract). INRA@RecSys 2021: 43-46 - [c112]Michael D. Ekstrand, Allison Chaney, Pablo Castells, Robin Burke, David Rohde, Manel Slokom:
SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. RecSys 2021: 803-805 - [c111]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, Edward C. Malthouse:
User-centered Evaluation of Popularity Bias in Recommender Systems. UMAP 2021: 119-129 - [c110]Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, Casey Fiesler:
Fairness and Transparency in Recommendation: The Users' Perspective. UMAP 2021: 274-279 - [i31]Xiaolei Huang, Michael J. Paul, Robin Burke, Franck Dernoncourt, Mark Dredze:
User Factor Adaptation for User Embedding via Multitask Learning. CoRR abs/2102.11103 (2021) - [i30]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, Edward C. Malthouse:
User-centered Evaluation of Popularity Bias in Recommender Systems. CoRR abs/2103.06364 (2021) - [i29]Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, Casey Fiesler:
Fairness and Transparency in Recommendation: The Users' Perspective. CoRR abs/2103.08786 (2021) - [i28]Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz:
Fairness and Discrimination in Information Access Systems. CoRR abs/2105.05779 (2021) - [i27]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems. CoRR abs/2107.03415 (2021) - [i26]Masoud Mansoury, Himan Abdollahpouri, Bamshad Mobasher, Mykola Pechenizkiy, Robin Burke, Milad Sabouri:
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation. CoRR abs/2108.03440 (2021) - 2020
- [j20]John Shanahan, Robin Burke, Ana Lucic:
Reading Chicago Reading: Quantitative Analysis of a Repeating Literary Program. Digit. Humanit. Q. 14(2) (2020) - [j19]Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato:
Multistakeholder recommendation: Survey and research directions. User Model. User Adapt. Interact. 30(1): 127-158 (2020) - [c109]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
Feedback Loop and Bias Amplification in Recommender Systems. CIKM 2020: 2145-2148 - [c108]Robin D. Burke, Masoud Mansoury, Nasim Sonboli:
Experimentation with fairness-aware recommendation using librec-auto: hands-on tutorial. FAT* 2020: 700 - [c107]Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke:
Calibration in Collaborative Filtering Recommender Systems: a User-Centered Analysis. HT 2020: 197-206 - [c106]Nasim Sonboli, Robin Burke, Zijun Liu, Masoud Mansoury:
Fairness-aware Recommendation with librec-auto. RecSys 2020: 594-596 - [c105]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation. RecSys 2020: 726-731 - [c104]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems. UMAP 2020: 154-162 - [c103]Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher:
Opportunistic Multi-aspect Fairness through Personalized Re-ranking. UMAP 2020: 239-247 - [c102]Diego Sánchez-Moreno, María N. Moreno García, Nasim Sonboli, Bamshad Mobasher, Robin Burke:
Using Social Tag Embedding in a Collaborative Filtering Approach for Recommender Systems. WI/IAT 2020: 502-507 - [e5]Tsvi Kuflik, Ilaria Torre, Robin Burke, Cristina Gena:
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2020, Genoa, Italy, July 12-18, 2020. ACM 2020, ISBN 978-1-4503-6861-2 [contents] - [e4]Tsvi Kuflik, Ilaria Torre, Robin Burke, Cristina Gena:
Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2020, Genoa, Italy, July 12-18, 2020. ACM 2020, ISBN 978-1-4503-7950-2 [contents] - [i25]Jessie Smith, Nasim Sonboli, Casey Fiesler, Robin Burke:
Exploring User Opinions of Fairness in Recommender Systems. CoRR abs/2003.06461 (2020) - [i24]Carole-Jean Wu, Robin Burke, Ed H. Chi, Joseph A. Konstan, Julian J. McAuley, Yves Raimond, Hao Zhang:
Developing a Recommendation Benchmark for MLPerf Training and Inference. CoRR abs/2003.07336 (2020) - [i23]Himan Abdollahpouri, Robin Burke, Masoud Mansoury:
Unfair Exposure of Artists in Music Recommendation. CoRR abs/2003.11634 (2020) - [i22]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems. CoRR abs/2005.01148 (2020) - [i21]Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher:
Opportunistic Multi-aspect Fairness through Personalized Re-ranking. CoRR abs/2005.12974 (2020) - [i20]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration. CoRR abs/2007.12230 (2020) - [i19]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
Feedback Loop and Bias Amplification in Recommender Systems. CoRR abs/2007.13019 (2020) - [i18]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation. CoRR abs/2008.09273 (2020) - [i17]Nasim Sonboli, Robin Burke, Nicholas Mattei, Farzad Eskandanian, Tian Gao:
"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation. CoRR abs/2009.02590 (2020)
2010 – 2019
- 2019
- [j18]Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke:
Research commentary on recommendations with side information: A survey and research directions. Electron. Commer. Res. Appl. 37 (2019) - [c101]Masoud Mansoury, Robin Burke:
Algorithm Selection with Librec-auto. AMIR@ECIR 2019: 11-17 - [c100]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. FLAIRS 2019: 413-418 - [c99]Ana Lucic, Robin Burke, John Shanahan:
Unsupervised Clustering with Smoothing for Detecting Paratext Boundaries in Scanned Documents. JCDL 2019: 53-56 - [c98]Himan Abdollahpouri, Robin Burke:
Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness. RMSE@RecSys 2019 - [c97]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Unfairness of Popularity Bias in Recommendation. RMSE@RecSys 2019 - [c96]Robin Burke, Himan Abdollahpouri, Edward C. Malthouse, K. P. Thai, Yongfeng Zhang:
Recommendation in multistakeholder environments. RecSys 2019: 566-567 - [c95]Robin Burke, Himan Abdollahpouri, Edward C. Malthouse, K. P. Thai, Yongfeng Zhang:
RMSE: Workshop on Recommendation in Multi-stakeholder Environments. RMSE@RecSys 2019 - [c94]Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, Shengyu Zhang:
Personalized fairness-aware re-ranking for microlending. RecSys 2019: 467-471 - [c93]Michael D. Ekstrand, Robin Burke, Fernando Diaz:
Fairness and discrimination in recommendation and retrieval. RecSys 2019: 576-577 - [c92]Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke:
Crank up the Volume: Preference Bias Amplification in Collaborative Recommendation. RMSE@RecSys 2019 - [c91]Edward C. Malthouse, Khadija Ali Vakeel, Yasaman Kamyab Hessary, Robin Burke, Morana Fuduric:
A Multistakeholder Recommender Systems Algorithm for Allocating Sponsored Recommendations. RMSE@RecSys 2019 - [c90]Masoud Mansoury, Bamshad Mobasher, Robin Burke, Mykola Pechenizkiy:
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison. RMSE@RecSys 2019 - [c89]Michael D. Ekstrand, Robin Burke, Fernando Diaz:
Fairness and Discrimination in Retrieval and Recommendation. SIGIR 2019: 1403-1404 - [c88]Bettina Berendt, Veronika Bogina, Robin Burke, Michael D. Ekstrand, Alan Hartman, Styliani Kleanthous, Tsvi Kuflik, Bamshad Mobasher, Jahna Otterbacher:
FairUMAP 2019 Chairs' Welcome Overview. UMAP (Adjunct Publication) 2019: 279-281 - [c87]Nasim Sonboli, Robin Burke:
Localized Fairness in Recommender Systems. UMAP (Adjunct Publication) 2019: 295-300 - [e3]Robin Burke, Himan Abdollahpouri, Edward C. Malthouse, K. P. Thai, Yongfeng Zhang:
Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019), Copenhagen, Denmark, September 20, 2019. CEUR Workshop Proceedings 2440, CEUR-WS.org 2019 [contents] - [i16]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking. CoRR abs/1901.07555 (2019) - [i15]Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato:
Beyond Personalization: Research Directions in Multistakeholder Recommendation. CoRR abs/1905.01986 (2019) - [i14]Himan Abdollahpouri, Robin Burke:
Reducing Popularity Bias in Recommendation Over Time. CoRR abs/1906.11711 (2019) - [i13]Masoud Mansoury, Robin Burke, Bamshad Mobasher:
Flatter is better: Percentile Transformations for Recommender Systems. CoRR abs/1907.07766 (2019) - [i12]Himan Abdollahpouri, Robin Burke:
Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness. CoRR abs/1907.13158 (2019) - [i11]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Unfairness of Popularity Bias in Recommendation. CoRR abs/1907.13286 (2019) - [i10]Masoud Mansoury, Bamshad Mobasher, Robin Burke, Mykola Pechenizkiy:
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison. CoRR abs/1908.00831 (2019) - [i9]Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke:
Crank up the volume: preference bias amplification in collaborative recommendation. CoRR abs/1909.06362 (2019) - [i8]Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke:
Research Commentary on Recommendations with Side Information: A Survey and Research Directions. CoRR abs/1909.12807 (2019) - [i7]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Impact of Popularity Bias on Fairness and Calibration in Recommendation. CoRR abs/1910.05755 (2019) - 2018
- [c86]Robin Burke, Nasim Sonboli, Aldo Ordonez-Gauger:
Balanced Neighborhoods for Multi-sided Fairness in Recommendation. FAT 2018: 202-214 - [c85]Diego Sánchez-Moreno, María N. Moreno García, Nasim Sonboli, Bamshad Mobasher, Robin Burke:
Inferring User Expertise from Social Tagging in Music Recommender Systems for Streaming Services. HAIS 2018: 39-49 - [c84]Özge Sürer, Robin Burke, Edward C. Malthouse:
Multistakeholder recommendation with provider constraints. RecSys 2018: 54-62 - [c83]Masoud Mansoury, Robin Burke, Aldo Ordonez-Gauger, Xavier Sepulveda:
Automating recommender systems experimentation with librec-auto. RecSys 2018: 500-501 - [c82]Bamshad Mobasher, Robin Burke, Michael D. Ekstrand, Bettina Berendt:
UMAP 2018 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2018) Chairs' Welcome & Organization: Preface. UMAP (Adjunct Publication) 2018: 3-5 - [r2]Fatemeh Vahedian, Robin Burke:
Recommender Systems Based on Social Networks. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i6]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Value-Aware Item Weighting for Long-Tail Recommendation. CoRR abs/1802.05382 (2018) - [i5]Weiwen Liu, Robin Burke:
Personalizing Fairness-aware Re-ranking. CoRR abs/1809.02921 (2018) - [i4]Robin Burke, Jackson Kontny, Nasim Sonboli:
Synthetic Attribute Data for Evaluating Consumer-side Fairness. CoRR abs/1809.04199 (2018) - 2017
- [j17]Fatemeh Vahedian, Robin Burke, Bamshad Mobasher:
Multirelational Recommendation in Heterogeneous Networks. ACM Trans. Web 11(3): 15:1-15:34 (2017) - [c81]Laura Christiansen, Bamshad Mobasher, Robin Burke:
Using Uncertain Graphs to Automatically Generate Event Flows from News Stories. HT (Extended Proceedings) 2017 - [c80]Robin Burke, Ana Lucic, John Shanahan:
Circulation Modeling of Library Book Promotions. JCDL 2017: 291-292 - [c79]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Controlling Popularity Bias in Learning-to-Rank Recommendation. RecSys 2017: 42-46 - [c78]Robin Burke, Gediminas Adomavicius, Ido Guy, Jan Krasnodebski, Luiz Augusto Pizzato, Yi Zhang, Himan Abdollahpouri:
VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation. RecSys 2017: 378-379 - [c77]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Weighted Random Walk Sampling for Multi-Relational Recommendation. UMAP 2017: 230-237 - [c76]Farzad Eskandanian, Bamshad Mobasher, Robin Burke:
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. UMAP 2017: 280-284 - [c75]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Recommender Systems as Multistakeholder Environments. UMAP 2017: 347-348 - [p3]Yong Zheng, Bamshad Mobasher, Robin Burke:
Emotions in Context-Aware Recommender Systems. Emotions and Personality in Personalized Services 2017: 311-326 - [i3]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Weighted Random Walk Sampling for Multi-Relational Recommendation. CoRR abs/1703.00034 (2017) - [i2]Robin Burke:
Multisided Fairness for Recommendation. CoRR abs/1707.00093 (2017) - [i1]Robin Burke, Himan Abdollahpouri:
Patterns of Multistakeholder Recommendation. CoRR abs/1707.09258 (2017) - 2016
- [c74]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Meta-Path Selection for Extended Multi-Relational Matrix Factorization. FLAIRS 2016: 566-571 - [c73]Farzad Eskandanian, Bamshad Mobasher, Robin D. Burke:
User Segmentation for Controlling Recommendation Diversity. RecSys Posters 2016 - [c72]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Weighted Random Walks for Meta-Path Expansion in Heterogeneous Networks. RecSys Posters 2016 - [c71]Robin D. Burke, Himan Abdollahpouri, Bamshad Mobasher, Trinadh Gupta:
Towards Multi-Stakeholder Utility Evaluation of Recommender Systems. UMAP (Extended Proceedings) 2016 - [c70]Yong Zheng, Bamshad Mobasher, Robin Burke:
User-Oriented Context Suggestion. UMAP 2016: 249-258 - [c69]Robin D. Burke, Himan Abdollahpouri:
Educational Recommendation with Multiple Stakeholders. WI Workshops 2016: 62-63 - [c68]Robin D. Burke, Farzad Eskandanian:
Collaborative Recommendation of Informal Learning Experiences. WI Workshops 2016: 66-67 - 2015
- [c67]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
CARSKit: A Java-Based Context-Aware Recommendation Engine. ICDM Workshops 2015: 1668-1671 - [c66]Negar Hariri, Bamshad Mobasher, Robin Burke:
Adapting to User Preference Changes in Interactive Recommendation. IJCAI 2015: 4268-4274 - [c65]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Incorporating Context Correlation into Context-aware Matrix Factorization. CPCR+ITWP@IJCAI 2015 - [c64]Mehdi Hosseinzadeh Aghdam, Negar Hariri, Bamshad Mobasher, Robin D. Burke:
Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models. RecSys 2015: 241-244 - [c63]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Network-Based Extension of Multi-Relational Factorization Models. RecSys Posters 2015 - [c62]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Integrating Context Similarity with Sparse Linear Recommendation Model. UMAP 2015: 370-376 - [c61]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Similarity-Based Context-Aware Recommendation. WISE (1) 2015: 431-447 - [r1]Robin Burke, Michael P. O'Mahony, Neil J. Hurley:
Robust Collaborative Recommendation. Recommender Systems Handbook 2015: 961-995 - 2014
- [c60]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Deviation-Based Contextual SLIM Recommenders. CIKM 2014: 271-280 - [c59]Jonathan Gemmell, Bamshad Mobasher, Robin D. Burke:
Resource Recommendation in Social Annotation Systems Based on User Partitioning. EC-Web 2014: 101-112 - [c58]Negar Hariri, Bamshad Mobasher, Robin D. Burke:
Context adaptation in interactive recommender systems. RecSys 2014: 41-48 - [c57]Fatemeh Vahedian, Robin D. Burke:
Predicting Component Utilities for Linear-Weighted Hybrid Recommendation. RSWeb@RecSys 2014 - [c56]