Contexto: lessons learned from mobile context inference. - Moshe Unger; Lior Rokach; Ariel Bar; Ehud Gudes; Bracha Shapira (UbiComp Adjunct, 2014)

Latent Context-Aware Recommender Systems. - Moshe Unger (RecSys, 2015)

Scalable attack propagation model and algorithms for honeypot systems. - Ariel Bar; Bracha Shapira; Lior Rokach; Moshe Unger (IEEE BigData, 2016)

Deep Auto-Encoding for Context-Aware Inference of Preferred Items' Categories. - Moshe Unger; Bracha Shapira; Lior Rokach; Ariel Bar (RecSys Posters, 2016)

Identifying Attack Propagation Patterns in Honeypots Using Markov Chains Modeling and Complex Networks Analysis. - Ariel Bar; Bracha Shapira; Lior Rokach; Moshe Unger (SwSTE, 2016)

Towards latent context-aware recommendation systems. - Moshe Unger; Ariel Bar; Bracha Shapira; Lior Rokach (Knowl. Based Syst., 2016)

Inferring Contextual Preferences Using Deep Auto-Encoding. - Moshe Unger; Bracha Shapira; Lior Rokach; Ariel Bar (UMAP, 2017)

Inferring contextual preferences using deep encoder-decoder learners. - Moshe Unger; Bracha Shapira; Lior Rokach; Amit Livne (New Rev. Hypermedia Multim., 2018)

Deep Context-Aware Recommender System Utilizing Sequential Latent Context. - Amit Livne; Moshe Unger; Bracha Shapira; Lior Rokach (CoRR, 2019)

Workshop on Context-Aware Recommender Systems. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Francesco Ricci 001; Alexander Tuzhilin; Moshe Unger (RecSys, 2020)

Sequence Preserving Network Traffic Generation. - Sigal Shaked; Amos Zamir; Roman Vainshtein; Moshe Unger; Lior Rokach; Rami Puzis; Bracha Shapira (CoRR, 2020)

Context-Aware Recommendations Based on Deep Learning Frameworks. - Moshe Unger; Alexander Tuzhilin; Amit Livne (ACM Trans. Manag. Inf. Syst., 2020)

Workshop on Context-Aware Recommender Systems (CARS) 2021. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Francesco Ricci 001; Alexander Tuzhilin; Moshe Unger (RecSys, 2021)

CARS: Workshop on Context-Aware Recommender Systems 2022. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Francesco Ricci 001; Alexander Tuzhilin; Moshe Unger (RecSys, 2022)

Hierarchical Latent Context Representation for Context-Aware Recommendations. - Moshe Unger; Alexander Tuzhilin (IEEE Trans. Knowl. Data Eng., 2022)

Context-Aware Recommender Systems: From Foundations to Recent Developments. - Gediminas Adomavicius; Konstantin Bauman; Alexander Tuzhilin; Moshe Unger (Recommender Systems Handbook, 2022)

Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract). - Moshe Unger; Alexander Tuzhilin (ICDE, 2023)

Workshop on Context-Aware Recommender Systems 2023. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Alexander Tuzhilin; Moshe Unger (RecSys, 2023)

Don't Need All Eggs in One Basket: Reconstructing Composite Embeddings of Customers from Individual-Domain Embeddings. - Moshe Unger; Pan Li 008; Sahana (Shahana) Sen; Alexander Tuzhilin (ACM Trans. Manag. Inf. Syst., 2023)

Workshop on Context-Aware Recommender Systems (CARS) 2024. - Gediminas Adomavicius; Konstantin Bauman; Bamshad Mobasher; Alexander Tuzhilin; Moshe Unger (RecSys, 2024)

Predicting consumer choice from raw eye-movement data using the RETINA deep learning architecture. - Moshe Unger; Michel Wedel; Alexander Tuzhilin (Data Min. Knowl. Discov., 2024)

HyperCARS: Using Hyperbolic Embeddings for Generating Hierarchical Contextual Situations in Context-Aware Recommender Systems. - Konstantin Bauman; Alexander Tuzhilin; Moshe Unger (Inf. Syst. Res., 2025)

STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements. - Moshe Unger; Alexander Tuzhilin; Michel Wedel (CoRR, 2025)