Moshe Unger

462 total citations
21 papers, 241 citations indexed

About

Moshe Unger is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Transportation. According to data from OpenAlex, Moshe Unger has authored 21 papers receiving a total of 241 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Information Systems, 11 papers in Computer Vision and Pattern Recognition and 8 papers in Transportation. Recurrent topics in Moshe Unger's work include Recommender Systems and Techniques (16 papers), Human Mobility and Location-Based Analysis (8 papers) and Context-Aware Activity Recognition Systems (6 papers). Moshe Unger is often cited by papers focused on Recommender Systems and Techniques (16 papers), Human Mobility and Location-Based Analysis (8 papers) and Context-Aware Activity Recognition Systems (6 papers). Moshe Unger collaborates with scholars based in Israel, United States and Italy. Moshe Unger's co-authors include Lior Rokach, Bracha Shapira, Alexander Tuzhilin, Ariel Bar, Michel Wedel, Gediminas Adomavičius, Konstantin Bauman, Bamshad Mobasher, Ehud Gudes and Pan Li and has published in prestigious journals such as Information Systems Research, IEEE Transactions on Knowledge and Data Engineering and Knowledge-Based Systems.

In The Last Decade

Moshe Unger

21 papers receiving 232 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Moshe Unger Israel	7	187	105	86	51	43	21	241
Wenguang Zheng China	9	168 0.9×	80 0.8×	57 0.7×	95 1.9×	29 0.7×	58	290
Suhas Ranganath United States	9	189 1.0×	129 1.2×	79 0.9×	45 0.9×	17 0.4×	19	304
Ariel Bar Israel	7	123 0.7×	129 1.2×	55 0.6×	123 2.4×	94 2.2×	12	260
Abdulmotaleb El-Saddik Canada	7	155 0.8×	84 0.8×	63 0.7×	85 1.7×	39 0.9×	24	274
Hai-Tao Yu Japan	8	163 0.9×	142 1.4×	61 0.7×	25 0.5×	20 0.5×	39	251
Neelam Duhan India	9	195 1.0×	139 1.3×	39 0.5×	40 0.8×	44 1.0×	45	298
Nan Zheng China	7	173 0.9×	85 0.8×	81 0.9×	29 0.6×	12 0.3×	16	253
Chongming Gao China	9	190 1.0×	185 1.8×	74 0.9×	23 0.5×	45 1.0×	27	327
Lejian Ren China	7	162 0.9×	104 1.0×	137 1.6×	24 0.5×	21 0.5×	8	254
Felice Antonio Merra Italy	8	149 0.8×	191 1.8×	87 1.0×	25 0.5×	21 0.5×	19	266

Countries citing papers authored by Moshe Unger

Since Specialization

Citations

This map shows the geographic impact of Moshe Unger's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Moshe Unger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Moshe Unger more than expected).

Fields of papers citing papers by Moshe Unger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Moshe Unger. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Moshe Unger. The network helps show where Moshe Unger may publish in the future.

Co-authorship network of co-authors of Moshe Unger

This figure shows the co-authorship network connecting the top 25 collaborators of Moshe Unger. A scholar is included among the top collaborators of Moshe Unger based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Moshe Unger. Moshe Unger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Adomavičius, Gediminas, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, & Moshe Unger. (2024). Workshop on Context-Aware Recommender Systems (CARS) 2024. 1219–1221. 1 indexed citations

Bauman, Konstantin, Alexander Tuzhilin, & Moshe Unger. (2024). HyperCARS: Using Hyperbolic Embeddings for Generating Hierarchical Contextual Situations in Context-Aware Recommender Systems. Information Systems Research. 36(2). 871–895. 2 indexed citations

Unger, Moshe, Michel Wedel, & Alexander Tuzhilin. (2023). Predicting Consumer Choice from Raw Eye-Movement Data Using the RETINA Deep Learning Architecture. SSRN Electronic Journal. 1 indexed citations

Unger, Moshe, et al.. (2023). Don’t Need All Eggs in One Basket: Reconstructing Composite Embeddings of Customers from Individual-Domain Embeddings. ACM Transactions on Management Information Systems. 14(2). 1–30. 1 indexed citations

Adomavičius, Gediminas, Konstantin Bauman, Bamshad Mobasher, Alexander Tuzhilin, & Moshe Unger. (2023). Workshop on Context-Aware Recommender Systems 2023. 1234–1236. 2 indexed citations

Unger, Moshe, Michel Wedel, & Alexander Tuzhilin. (2023). Predicting consumer choice from raw eye-movement data using the RETINA deep learning architecture. Data Mining and Knowledge Discovery. 38(3). 1069–1100. 6 indexed citations

Adomavičius, Gediminas, Konstantin Bauman, Bamshad Mobasher, et al.. (2021). Workshop on Context-Aware Recommender Systems (CARS) 2021. View. 813–814. 1 indexed citations

Unger, Moshe, et al.. (2021). Deep Multi-Objective Multi-Stakeholder Music Recommendation. SSRN Electronic Journal. 2 indexed citations

Unger, Moshe & Alexander Tuzhilin. (2020). Hierarchical Latent Context Representation for Context-Aware Recommendations. IEEE Transactions on Knowledge and Data Engineering. 1–1. 16 indexed citations

10.

Unger, Moshe, et al.. (2020). Context-Aware Recommendations Based on Deep Learning Frameworks. ACM Transactions on Management Information Systems. 11(2). 1–15. 56 indexed citations

11.

Adomavičius, Gediminas, Konstantin Bauman, Bamshad Mobasher, et al.. (2020). Workshop on Context-Aware Recommender Systems. View. 635–637. 1 indexed citations

12.

Adomavičius, Gediminas, Konstantin Bauman, Bamshad Mobasher, et al.. (2019). Workshop on context-aware recommender systems. View. 548–549. 2 indexed citations

13.

Unger, Moshe, et al.. (2018). Inferring contextual preferences using deep encoder-decoder learners. New Review of Hypermedia and Multimedia. 24(3). 262–290. 7 indexed citations

14.

Unger, Moshe, Bracha Shapira, Lior Rokach, & Ariel Bar. (2017). Inferring Contextual Preferences Using Deep Auto-Encoding. 221–229. 15 indexed citations

15.

Unger, Moshe, Bracha Shapira, Lior Rokach, & Ariel Bar. (2016). Deep Auto-Encoding for Context-Aware Inference of Preferred Items' Categories.. Conference on Recommender Systems. 1 indexed citations

16.

Unger, Moshe, Ariel Bar, Bracha Shapira, & Lior Rokach. (2016). Towards latent context-aware recommendation systems. Knowledge-Based Systems. 104. 165–178. 81 indexed citations

17.

Bar, Ariel, Bracha Shapira, Lior Rokach, & Moshe Unger. (2016). Identifying Attack Propagation Patterns in Honeypots Using Markov Chains Modeling and Complex Networks Analysis. 28–36. 23 indexed citations

18.

Bar, Ariel, Bracha Shapira, Lior Rokach, & Moshe Unger. (2016). Scalable attack propagation model and algorithms for honeypot systems. 4. 1130–1135. 3 indexed citations

19.

Unger, Moshe. (2015). Latent Context-Aware Recommender Systems. 383–386. 13 indexed citations

20.

Unger, Moshe, Ariel Bar, Bracha Shapira, Lior Rokach, & Ehud Gudes. (2014). Contexto. 175–178. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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