| Towards latent context-aware recommendation systems M Unger, A Bar, B Shapira, L Rokach Knowledge-Based Systems 104, 165-178, 2016 | 165 | 2016 |
| Context-aware recommendations based on deep learning frameworks M Unger, A Tuzhilin, A Livne ACM Transactions on Management Information Systems (TMIS) 11 (2), 1-15, 2020 | 87 | 2020 |
| Context-aware recommender systems: From foundations to recent developments G Adomavicius, K Bauman, A Tuzhilin, M Unger Recommender systems handbook, 211-250, 2021 | 69 | 2021 |
| Identifying attack propagation patterns in honeypots using Markov chains modeling and complex networks analysis A Bar, B Shapira, L Rokach, M Unger 2016 IEEE international conference on software science, technology and …, 2016 | 42 | 2016 |
| Latent context-aware recommender systems M Unger Proceedings of the 9th ACM Conference on Recommender Systems, 383-386, 2015 | 34 | 2015 |
| Inferring contextual preferences using deep auto-encoding M Unger, B Shapira, L Rokach, A Bar Proceedings of the 25th Conference on User Modeling, Adaptation and …, 2017 | 27 | 2017 |
| Deep context-aware recommender system utilizing sequential latent context A Livne, M Unger, B Shapira, L Rokach arXiv preprint arXiv:1909.03999, 2019 | 26 | 2019 |
| Hierarchical latent context representation for context-aware recommendations M Unger, A Tuzhilin IEEE Transactions on Knowledge and Data Engineering 34 (7), 3322-3334, 2020 | 23 | 2020 |
| Inferring contextual preferences using deep encoder-decoder learners M Unger, B Shapira, L Rokach, A Livne New Review of Hypermedia and Multimedia 24 (3), 262-290, 2018 | 12 | 2018 |
| Scalable attack propagation model and algorithms for honeypot systems A Bar, B Shapira, L Rokach, M Unger 2016 IEEE International Conference on Big Data (Big Data), 1130-1135, 2016 | 11 | 2016 |
| Bridging Listeners with Artists: Deep Multi-Objective Multi-Stakeholder Music Recommendations M Unger, P Li, MC Cohen, B Brost, A Tuzhilin NYU Stern School of Business Forthcoming, 2021 | 10 | 2021 |
| Predicting consumer choice from raw eye-movement data using the RETINA deep learning architecture M Unger, M Wedel, A Tuzhilin Data Mining and Knowledge Discovery 38 (3), 1069-1100, 2024 | 8 | 2024 |
| Contexto: lessons learned from mobile context inference M Unger, A Bar, B Shapira, L Rokach, E Gudes Proceedings of the 2014 ACM International Joint Conference on Pervasive and …, 2014 | 7 | 2014 |
| HyperCARS: Using Hyperbolic Embeddings for Generating Hierarchical Contextual Situations in Context-Aware Recommender Systems K Bauman, A Tuzhilin, M Unger Information Systems Research, 2024 | 5 | 2024 |
| Workshop on context-aware recommender systems G Adomavicius, K Bauman, B Mobasher, F Ricci, A Tuzhilin, M Unger Proceedings of the 14th ACM Conference on Recommender Systems, 635-637, 2020 | 4 | 2020 |
| Don’t need all eggs in one basket: Reconstructing composite embeddings of customers from individual-domain embeddings M Unger, P Li, S Sen, A Tuzhilin ACM Transactions on Management Information Systems 14 (2), 1-30, 2023 | 3 | 2023 |
| Hierarchical latent context representation for CARS M Unger, A Tuzhilin Stern School of Business, New York University, 2019 | 3 | 2019 |
| Deep Auto-Encoding for Context-Aware Inference of Preferred Items' Categories. M Unger, B Shapira, L Rokach, A Bar RecSys Posters, 2016 | 3 | 2016 |
| Workshop on Context-Aware Recommender Systems 2023 G Adomavicius, K Bauman, B Mobasher, A Tuzhilin, M Unger Proceedings of the 17th ACM Conference on Recommender Systems, 1234-1236, 2023 | 2 | 2023 |
| Sequence preserving network traffic generation S Shaked, A Zamir, R Vainshtein, M Unger, L Rokach, R Puzis, B Shapira arXiv preprint arXiv:2002.09832, 2020 | 1 | 2020 |
Bracha ShapiraBen-Gurion University of the NegevDirección de correo verificada de bgu.ac.il
