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ORCIDBamshad Mobasher
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- affiliation: DePaul University, Chicago, IL, USA
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2020 – today
- 2024
[c146]Kun Lin
, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher:
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation. UMAP (Adjunct Publication) 2024- [i32]Kun Lin, Masoud Mansoury, Farzad Eskandanian, Milad Sabouri, Bamshad Mobasher:
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation. CoRR abs/2405.10232 (2024) - 2023
- [c145]Payam Pourashraf, Bamshad Mobasher:
Modeling Users' Localized Preferences for More Effective News Recommendation. HCI (41) 2023: 366-382 - [c144]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems 2023. RecSys 2023: 1234-1236 - [c143]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 - [i31]Masoud Mansoury, Bamshad Mobasher:
Fairness of Exposure in Dynamic Recommendation. CoRR abs/2309.02322 (2023) - 2022
- [j40]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) - [c142]Shoujin Wang, Ninghao Liu, Xiuzhen Zhang, Yan Wang, Francesco Ricci, Bamshad Mobasher:
Data Science and Artificial Intelligence for Responsible Recommendations. KDD 2022: 4904-4905 - [c141]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
CARS: Workshop on Context-Aware Recommender Systems 2022. RecSys 2022: 691-693 - [c140]Payam Pourashraf, Bamshad Mobasher:
Using Recommender Systems to Help Revitalize Local News. UMAP (Adjunct Publication) 2022: 80-84 - [c139]Mohammed Muheeb Ghori, Arman Dehpanah, Jonathan Gemmell, Hamed Qahri-Saremi, Bamshad Mobasher:
Does the User Have A Theory of the Recommender? A Grounded Theory Study. UMAP (Adjunct Publication) 2022: 167-174 - [c138]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 - [c137]Payam Pourashraf, Bamshad Mobasher:
Using user's local context to support local news. UMAP (Adjunct Publication) 2022: 359-365 - [i30]Payam Pourashraf, Bamshad Mobasher:
Using user's local context to support local news. CoRR abs/2205.12408 (2022) - [i29]Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher:
Behavioral Player Rating in Competitive Online Shooter Games. CoRR abs/2207.00528 (2022) - [i28]Masoud Mansoury, Bamshad Mobasher, Herke van Hoof:
Exposure-Aware Recommendation using Contextual Bandits. CoRR abs/2209.01665 (2022) - 2021
- [j39]Vito Walter Anelli, Pierpaolo Basile, Toine Bogers, Tommaso Di Noia, Francesco Maria Donini, Bamshad Mobasher, Cataldo Musto, Fedelucio Narducci, Casper Petersen, Maria Soledad Pera, Markus Zanker:
Report on the 3rd workshop of knowledge-aware and conversational recommender systems (KARS/ComplexRec) at RecSys 2021. SIGIR Forum 55(2): 17:1-17:9 (2021) - [j38]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) - [c136]Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher:
Evaluating Team Skill Aggregation in Online Competitive Games. CoG 2021: 1-8 - [c135]Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher:
Player Modeling using Behavioral Signals in Competitive Online Games. CSCI 2021: 569-574 - [c134]Muheeb Faizan Ghori, Arman Dehpanah, Jonathan Gemmell, Hamed Qahri-Saremi, Bamshad Mobasher:
How does the User's Knowledge of the Recommender Influence their Behavior? IntRS@RecSys 2021: 38-54 - [c133]Himan Abdollahpouri, Toine Bogers, Bamshad Mobasher, Casper Petersen, Maria Soledad Pera:
ComplexRec 2021: Fifth Workshop on Recommendation in Complex Environments. RecSys 2021: 775-777 - [c132]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems (CARS) 2021. RecSys 2021: 813-814 - [c131]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, Edward C. Malthouse:
User-centered Evaluation of Popularity Bias in Recommender Systems. UMAP 2021: 119-129 - [c130]Bamshad Mobasher, Styliani Kleanthous, Bettina Berendt, Jahna Otterbacher, Tsvi Kuflik, Avital Shulner-Tal:
FairUMAP 2021: The 4th Workshop on Fairness in User Modeling, Adaptation and Personalization. UMAP (Adjunct Publication) 2021: 399-400 - [c129]Himan Abdollahpouri, Edward C. Malthouse, Joseph A. Konstan, Bamshad Mobasher, Jeremy Gilbert:
Toward the Next Generation of News Recommender Systems. WWW (Companion Volume) 2021: 402-406 - [e19]Vito Walter Anelli, Pierpaolo Basile, Tommaso Di Noia, Francesco Maria Donini, Cataldo Musto, Fedelucio Narducci, Markus Zanker, Himan Abdollahpouri, Toine Bogers, Bamshad Mobasher, Casper Petersen, Maria Soledad Pera:
Joint Workshop Proceedings of the 3rd Edition of Knowledge-aware and Conversational Recommender Systems (KaRS) and the 5th Edition of Recommendation in Complex Environments (ComplexRec) co-located with 15th ACM Conference on Recommender Systems (RecSys 2021), Virtual Event, Amsterdam, The Netherlands, September 25, 2021. CEUR Workshop Proceedings 2960, CEUR-WS.org 2021 [contents] - [i27]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) - [i26]Himan Abdollahpouri, Edward C. Malthouse, Joseph A. Konstan, Bamshad Mobasher, Jeremy Gilbert:
Toward the Next Generation of News Recommender Systems. CoRR abs/2103.06909 (2021) - [i25]Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher:
The Evaluation of Rating Systems in Team-based Battle Royale Games. CoRR abs/2105.14069 (2021) - [i24]Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher:
Evaluating Team Skill Aggregation in Online Competitive Games. CoRR abs/2106.11397 (2021) - [i23]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) - [i22]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) - [i21]Muheeb Faizan Ghori, Arman Dehpanah, Jonathan Gemmell, Hamed Qahri-Saremi, Bamshad Mobasher:
How does the User's Knowledge of the Recommender Influence their Behavior? CoRR abs/2109.00982 (2021) - [i20]Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher:
Player Modeling using Behavioral Signals in Competitive Online Games. CoRR abs/2112.04379 (2021) - 2020
- [j37]Toine Bogers, Marijn Koolen, Bamshad Mobasher, Casper Petersen, Alexander Tuzhilin:
Report on the fourth workshop on recommendation in complex environments: (ComplexRec 2020). SIGIR Forum 54(2): 12:1-12:7 (2020) - [j36]Dietmar Jannach, Bamshad Mobasher, Shlomo Berkovsky:
Research directions in session-based and sequential recommendation. User Model. User Adapt. Interact. 30(4): 609-616 (2020) - [c128]Cynthia Putnam, Bamshad Mobasher:
Children with Autism and Technology Use: A Case Study of the Diary Method. CHI Extended Abstracts 2020: 1-8 - [c127]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
Feedback Loop and Bias Amplification in Recommender Systems. CIKM 2020: 2145-2148 - [c126]Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, Bamshad Mobasher:
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems. FLAIRS 2020: 193-196 - [c125]Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke:
Calibration in Collaborative Filtering Recommender Systems: a User-Centered Analysis. HT 2020: 197-206 - [c124]Toine Bogers, Marijn Koolen, Casper Petersen, Bamshad Mobasher, Alexander Tuzhilin:
ComplexRec 2020: Workshop on Recommendation in Complex Environments. RecSys 2020: 609-610 - [c123]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
Workshop on Context-Aware Recommender Systems. RecSys 2020: 635-637 - [c122]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation. RecSys 2020: 726-731 - [c121]Farzad Eskandanian, Bamshad Mobasher:
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation. UMAP 2020: 71-79 - [c120]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 - [c119]Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher:
Opportunistic Multi-aspect Fairness through Personalized Re-ranking. UMAP 2020: 239-247 - [c118]Bamshad Mobasher, Styliani Kleanthous, Bettina Berendt, Michael D. Ekstrand, Jahna Otterbacher, Avital Shulner-Tal:
UMAP 2020 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2020) Chairs' Welcome. UMAP (Adjunct Publication) 2020: 241-243 - [c117]Bamshad Mobasher, Styliani Kleanthous, Michael D. Ekstrand, Bettina Berendt, Jahna Otterbacher, Avital Shulner-Tal:
FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization. UMAP 2020: 404-405 - [c116]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 - [e18]Toine Bogers, Marijn Koolen, Casper Petersen, Bamshad Mobasher, Alexander Tuzhilin, Oren Sar Shalom, Dietmar Jannach, Joseph A. Konstan:
Proceedings of the Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems co-located with 14th ACM Conference on Recommender Systems (RecSys 2020), Online, September 25, 2020. CEUR Workshop Proceedings 2697, CEUR-WS.org 2020 [contents] - [i19]Masoud Mansoury, Himan Abdollahpouri, Jessie Smith, Arman Dehpanah, Mykola Pechenizkiy, Bamshad Mobasher:
Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems. CoRR abs/2002.07786 (2020) - [i18]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) - [i17]Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher:
Opportunistic Multi-aspect Fairness through Personalized Re-ranking. CoRR abs/2005.12974 (2020) - [i16]Farzad Eskandanian, Bamshad Mobasher:
Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation. CoRR abs/2006.03715 (2020) - [i15]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration. CoRR abs/2007.12230 (2020) - [i14]Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke:
Feedback Loop and Bias Amplification in Recommender Systems. CoRR abs/2007.13019 (2020) - [i13]Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher:
The Evaluation of Rating Systems in Online Free-for-All Games. CoRR abs/2008.06787 (2020) - [i12]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation. CoRR abs/2008.09273 (2020)
2010 – 2019
- 2019
- [j35]Bamshad Mobasher, Lucia Dettori, Daniela Raicu, Raffaella Settimi, Nasim Sonboli, Monica Stettler:
Data Science Summer Academy for Chicago Public School Students. SIGKDD Explor. 21(1): 49-52 (2019) - [c115]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. FLAIRS 2019: 413-418 - [c114]Farzad Eskandanian, Bamshad Mobasher:
Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models. FLAIRS 2019: 425-430 - [c113]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Unfairness of Popularity Bias in Recommendation. RMSE@RecSys 2019 - [c112]Marijn Koolen, Toine Bogers, Bamshad Mobasher, Alexander Tuzhilin:
Overview of the Workshop on Recommendation in Complex Scenarios 2019 (ComplexRec 2019). ComplexRec@RecSys 2019: 1-3 - [c111]Muheeb Faizan Ghori, Arman Dehpanah, Jonathan Gemmell, Hamed Qahri-Saremi, Bamshad Mobasher:
Does the User Have A Theory of the Recommender? A Pilot Study. IntRS@RecSys 2019: 77-85 - [c110]Gediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, Moshe Unger:
Workshop on context-aware recommender systems. RecSys 2019: 548-549 - [c109]Marijn Koolen, Toine Bogers, Bamshad Mobasher, Alexander Tuzhilin:
Third workshop on recommendation in complex scenarios (ComplexRec 2019). RecSys 2019: 550-551 - [c108]Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke:
Crank up the Volume: Preference Bias Amplification in Collaborative Recommendation. RMSE@RecSys 2019 - [c107]Masoud Mansoury, Bamshad Mobasher, Robin Burke, Mykola Pechenizkiy:
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison. RMSE@RecSys 2019 - [c106]Geert-Jan Houben, Bamshad Mobasher:
UMAP 2019 Theory, Reflection, and Opinion Track: Chairs' Welcome and Overview. UMAP (Adjunct Publication) 2019: 1 - [c105]Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher:
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. UMAP 2019: 225-233 - [c104]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 - [e17]Marijn Koolen, Toine Bogers, Bamshad Mobasher, Alexander Tuzhilin:
Proceedings of the Workshop on Recommendation in Complex Scenarios co-located with 13th ACM Conference on Recommender Systems (RecSys 2019), Copenhagen, Denmark, September 20, 2019. CEUR Workshop Proceedings 2449, CEUR-WS.org 2019 [contents] - [i11]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking. CoRR abs/1901.07555 (2019) - [i10]Farzad Eskandanian, Bamshad Mobasher:
Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models. CoRR abs/1905.06863 (2019) - [i9]Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher:
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems. CoRR abs/1905.08031 (2019) - [i8]Masoud Mansoury, Robin Burke, Bamshad Mobasher:
Flatter is better: Percentile Transformations for Recommender Systems. CoRR abs/1907.07766 (2019) - [i7]Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher:
The Unfairness of Popularity Bias in Recommendation. CoRR abs/1907.13286 (2019) - [i6]Masoud Mansoury, Bamshad Mobasher, Robin Burke, Mykola Pechenizkiy:
Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison. CoRR abs/1908.00831 (2019) - [i5]Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke:
Crank up the volume: preference bias amplification in collaborative recommendation. CoRR abs/1909.06362 (2019) - [i4]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
- [c103]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 - [c102]Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Casper Petersen:
2nd workshop on recommendation in complex scenarios (complexrec 2018). RecSys 2018: 510-511 - [c101]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 - [p3]Yong Zheng, Bamshad Mobasher:
Context-Aware Recommendations. Collaborative Recommendations 2018: 173-202 - [i3]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Value-Aware Item Weighting for Long-Tail Recommendation. CoRR abs/1802.05382 (2018) - [i2]Farzad Eskandanian, Bamshad Mobasher:
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks. CoRR abs/1810.00272 (2018) - 2017
- [j34]Fatemeh Vahedian, Robin Burke, Bamshad Mobasher:
Multirelational Recommendation in Heterogeneous Networks. ACM Trans. Web 11(3): 15:1-15:34 (2017) - [c100]Laura Christiansen, Bamshad Mobasher, Robin Burke:
Using Uncertain Graphs to Automatically Generate Event Flows from News Stories. HT (Extended Proceedings) 2017 - [c99]Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin:
Workshop on Recommendation in Complex Scenarios (ComplexRec 2017). ComplexRec@RecSys 2017: 1-2 - [c98]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Controlling Popularity Bias in Learning-to-Rank Recommendation. RecSys 2017: 42-46 - [c97]Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin:
Workshop on Recommendation in Complex Scenarios: (ComplexRec 2017). RecSys 2017: 380-381 - [c96]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Weighted Random Walk Sampling for Multi-Relational Recommendation. UMAP 2017: 230-237 - [c95]Farzad Eskandanian, Bamshad Mobasher, Robin Burke:
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. UMAP 2017: 280-284 - [c94]Himan Abdollahpouri, Robin Burke, Bamshad Mobasher:
Recommender Systems as Multistakeholder Environments. UMAP 2017: 347-348 - [c93]Cataldo Musto, Amon Rapp, Veronika Bogina, Federica Cena, Frank Hopfgartner, Judy Kay, David Konopnicki, Tsvi Kuflik, Bamshad Mobasher, Giovanni Semeraro:
UMAP 2017 THUM Workshop Chairs' Welcome & Organization. UMAP (Adjunct Publication) 2017: 368-369 - [p2]Yong Zheng, Bamshad Mobasher, Robin Burke:
Emotions in Context-Aware Recommender Systems. Emotions and Personality in Personalized Services 2017: 311-326 - [e16]Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin:
Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios co-located with 11th ACM Conference on Recommender Systems (RecSys 2017), Como, Italy, August 31, 2017. CEUR Workshop Proceedings 1892, CEUR-WS.org 2017 [contents] - [i1]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Weighted Random Walk Sampling for Multi-Relational Recommendation. CoRR abs/1703.00034 (2017) - 2016
- [c92]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Meta-Path Selection for Extended Multi-Relational Matrix Factorization. FLAIRS 2016: 566-571 - [c91]Farzad Eskandanian, Bamshad Mobasher, Robin D. Burke:
User Segmentation for Controlling Recommendation Diversity. RecSys Posters 2016 - [c90]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Weighted Random Walks for Meta-Path Expansion in Heterogeneous Networks. RecSys Posters 2016 - [c89]Robin D. Burke, Himan Abdollahpouri, Bamshad Mobasher, Trinadh Gupta:
Towards Multi-Stakeholder Utility Evaluation of Recommender Systems. UMAP (Extended Proceedings) 2016 - [c88]Yong Zheng, Bamshad Mobasher, Robin Burke:
User-Oriented Context Suggestion. UMAP 2016: 249-258 - 2015
- [c87]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
CARSKit: A Java-Based Context-Aware Recommendation Engine. ICDM Workshops 2015: 1668-1671 - [c86]Negar Hariri, Bamshad Mobasher, Robin Burke:
Adapting to User Preference Changes in Interactive Recommendation. IJCAI 2015: 4268-4274 - [c85]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Incorporating Context Correlation into Context-aware Matrix Factorization. CPCR+ITWP@IJCAI 2015 - [c84]Mehdi Hosseinzadeh Aghdam, Negar Hariri, Bamshad Mobasher, Robin D. Burke:
Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models. RecSys 2015: 241-244 - [c83]Fatemeh Vahedian, Robin D. Burke, Bamshad Mobasher:
Network-Based Extension of Multi-Relational Factorization Models. RecSys Posters 2015 - [c82]Chuan Duan, Horatiu Dumitru, Jane Cleland-Huang, Bamshad Mobasher:
User-Constrained Clustering in Online Requirements Forums. REFSQ 2015: 284-299 - [c81]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Integrating Context Similarity with Sparse Linear Recommendation Model. UMAP 2015: 370-376 - [c80]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Similarity-Based Context-Aware Recommendation. WISE (1) 2015: 431-447 - [e15]Dietmar Jannach, Jérôme Mengin, Bamshad Mobasher, Andrea Passerini, Paolo Viappiani:
Proceedings of the IJCAI 2015 Joint Workshop on Constraints and Preferences for Configuration and Recommendation and Intelligent Techniques for Web Personalization co-located with the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 27, 2015. CEUR Workshop Proceedings 1440, CEUR-WS.org 2015 [contents] - 2014
- [c79]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Deviation-Based Contextual SLIM Recommenders. CIKM 2014: 271-280 - [c78]Jonathan Gemmell, Bamshad Mobasher, Robin D. Burke:
Resource Recommendation in Social Annotation Systems Based on User Partitioning. EC-Web 2014: 101-112 - [c77]Xavier Amatriain, Bamshad Mobasher:
The recommender problem revisited: morning tutorial. KDD 2014: 1971 - [c76]Negar Hariri, Bamshad Mobasher, Robin D. Burke:
Context adaptation in interactive recommender systems. RecSys 2014: 41-48 - [c75]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
CSLIM: contextual SLIM recommendation algorithms. RecSys 2014: 301-304 - [c74]Dietmar Jannach, Jill Freyne, Werner Geyer, Ido Guy, Andreas Hotho, Bamshad Mobasher:
The sixth ACM RecSys workshop on recommender systems and the social web. RecSys 2014: 395 - [c73]Yong Zheng, Robin D. Burke, Bamshad Mobasher:
Splitting approaches for context-aware recommendation: an empirical study. SAC 2014: 274-279 - [c72]