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Konstantin Bauman
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2020 – today
- 2024
- [c13]Gediminas Adomavicius
, Konstantin Bauman
Workshop on Context-Aware Recommender Systems (CARS) 2024. RecSys 2024: 1219-1221 - [c12]Konstantin Bauman
Does the Long Tail of Context Exist and Matter? The Case of Dialogue-based Recommender Systems. UMAP 2024: 273-278 - 2023
- [c11]Guohou Shan, Konstantin Bauman, Michael Rivera, Subodha Kumar:
Mobile or Desktop? That is the Question: An Empirical Study of the Role of Device Type on Real-time Employee Feedback Quality. AMCIS 2023 - [c10]Gediminas Adomavicius
Workshop on Context-Aware Recommender Systems 2023. RecSys 2023: 1234-1236 - 2022
- [j3]Konstantin Bauman
Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes. Inf. Syst. Res. 33(1): 179-202 (2022) - [c9]Gediminas Adomavicius, Konstantin Bauman
CARS: Workshop on Context-Aware Recommender Systems 2022. RecSys 2022: 691-693 - [r1]Gediminas Adomavicius, Konstantin Bauman
Context-Aware Recommender Systems: From Foundations to Recent Developments. Recommender Systems Handbook 2022: 211-250 - [i3]Konstantin Bauman
The Long Tail of Context: Does it Exist and Matter? CoRR abs/2210.01023 (2022) - 2021
- [c8]Gediminas Adomavicius, Konstantin Bauman
Workshop on Context-Aware Recommender Systems (CARS) 2021. RecSys 2021: 813-814 - 2020
- [c7]Gediminas Adomavicius, Konstantin Bauman
Workshop on Context-Aware Recommender Systems. RecSys 2020: 635-637
2010 – 2019
- 2019
- [c6]Gediminas Adomavicius, Konstantin Bauman
Workshop on context-aware recommender systems. RecSys 2019: 548-549 - 2018
- [j2]Konstantin Bauman
Recommending Remedial Learning Materials to Students by Filling Their Knowledge Gaps. MIS Q. 42(1) (2018) - 2017
- [j1]Konstantin Bauman
Using Social Sensors for Detecting Emergency Events: A Case of Power Outages in the Electrical Utility Industry. ACM Trans. Manag. Inf. Syst. 8(2-3): 7:1-7:20 (2017) - [c5]Evgeny Bauman, Konstantin Bauman
One-Class Semi-supervised Learning. Braverman Readings in Machine Learning 2017: 189-200 - [c4]Konstantin Bauman
Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews. KDD 2017: 717-725 - [i2]Evgeny Bauman, Konstantin Bauman:
One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean. CoRR abs/1705.00797 (2017) - [i1]Evgeny Bauman, Konstantin Bauman:
Discovering the Graph Structure in the Clustering Results. CoRR abs/1705.06753 (2017) - 2016
- [c3]Konstantin Bauman, Bing Liu, Alexander Tuzhilin:
Recommending Items with Conditions Enhancing User Experiences Based on Sentiment Analysis of Reviews. CBRecSys@RecSys 2016: 19-22 - 2014
- [c2]Konstantin Bauman, Alexander Tuzhilin:
Discovering Contextual Information from User Reviews for Recommendation Purposes. CBRecSys@RecSys 2014: 2-9 - [c1]Konstantin Bauman, Alexander Tuzhilin:
Recommending Learning Materials to Students by Identifying their Knowledge Gaps. RecSys Posters 2014
Coauthor Index
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