Bracha Shapira

11.5k total citations · 3 hit papers
163 papers, 5.2k citations indexed

About

Bracha Shapira is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Bracha Shapira has authored 163 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Information Systems, 72 papers in Artificial Intelligence and 26 papers in Computer Networks and Communications. Recurrent topics in Bracha Shapira's work include Recommender Systems and Techniques (47 papers), Topic Modeling (21 papers) and Network Security and Intrusion Detection (13 papers). Bracha Shapira is often cited by papers focused on Recommender Systems and Techniques (47 papers), Topic Modeling (21 papers) and Network Security and Intrusion Detection (13 papers). Bracha Shapira collaborates with scholars based in Israel, United States and United Kingdom. Bracha Shapira's co-authors include Lior Rokach, Francesco Ricci⋆, Bernard Lerer, Peretz Shoval, Yuval Elovici, Uri Hanani, Ariel Bar, Ofer Arazy, Nanda Kumar and Uriel Heresco‐Levy and has published in prestigious journals such as Bioinformatics, PLoS ONE and American Journal of Psychiatry.

In The Last Decade

Bracha Shapira

154 papers receiving 4.9k citations

Hit Papers

Recommender Systems Handbook 2015 2026 2018 2022 2015 2021 2022 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Recommender Systems Handbook
    2015 1.0k citations Francesco Ricci⋆, Lior Rokach et al. View profile →
  2. Explaining anomalies detected by autoencoders using Shapley Additive Explanations
    2021 259 citations Bracha Shapira, Lior Rokach et al. Expert Systems with Applications profile →
  3. Recommender Systems Handbook
    2022 222 citations Francesco Ricci⋆, Lior Rokach et al. profile →

Peers

Bracha Shapira
m.c. schraefel United Kingdom
Mark Weiser Israel
Ning Zhong China
Krzysztof Z. Gajos United States
Mary Czerwinski United States
Lina Yao Australia
Peter W. Foltz United States
Barry Smyth Ireland
Melanie Swan United States
Ondřej Krejcar Czechia
m.c. schraefel United Kingdom
Bracha Shapira
Citations per year, relative to Bracha Shapira Bracha Shapira (= 1×) peers m.c. schraefel

Countries citing papers authored by Bracha Shapira

Since Specialization
Citations

This map shows the geographic impact of Bracha Shapira'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 Bracha Shapira with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bracha Shapira more than expected).

Fields of papers citing papers by Bracha Shapira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bracha Shapira. 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 Bracha Shapira. The network helps show where Bracha Shapira may publish in the future.

Co-authorship network of co-authors of Bracha Shapira

This figure shows the co-authorship network connecting the top 25 collaborators of Bracha Shapira. A scholar is included among the top collaborators of Bracha Shapira 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 Bracha Shapira. Bracha Shapira is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Guy, Ido, et al.. (2025). What is in a title? Characterizing product titles in e-commerce. Expert Systems with Applications. 287. 127702–127702.
2.
Shapira, Bracha, et al.. (2024). Trends of common laboratory biomarkers after SARS-CoV-2 infection. Journal of Infection. 89(6). 106318–106318.
3.
Feinshtein, Valeria, et al.. (2024). Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model. Briefings in Bioinformatics. 25(3). 4 indexed citations
4.
Rokach, Lior, et al.. (2023). Shapley-based feature augmentation. Information Fusion. 96. 92–102. 12 indexed citations
5.
Bar, Ariel, Bracha Shapira, & Lior Rokach. (2023). Context aware Markov chains models. Knowledge-Based Systems. 282. 111083–111083.
6.
Shapira, Bracha, et al.. (2023). Molecule generation using transformers and policy gradient reinforcement learning. Scientific Reports. 13(1). 8799–8799. 31 indexed citations
7.
Shapira, Bracha, et al.. (2019). NO-DOUBT: Attack Attribution Based On Threat Intelligence Reports. 80–85. 15 indexed citations
8.
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
9.
Shapira, Bracha, et al.. (2016). Language Models with GloVe Word Embeddings.. arXiv (Cornell University). 1 indexed citations
10.
Guri, Mordechai, et al.. (2015). JoKER: Trusted Detection of Kernel Rootkits in Android Devices via JTAG Interface. 2015 IEEE Trustcom/BigDataSE/ISPA. 65–73. 22 indexed citations
11.
Ricci⋆, Francesco, Lior Rokach, & Bracha Shapira. (2015). Recommender Systems Handbook. View. 1042 indexed citations breakdown →
12.
Shapira, Bracha, et al.. (2015). Recommender Systems for Product Bundling.. Conference on Recommender Systems. 1 indexed citations
13.
Segal, Avi, et al.. (2014). EduRank: A Collaborative Filtering Approach to Personalization in E-learning.. Educational Data Mining. 68–75. 31 indexed citations
14.
Rokach, Lior, et al.. (2012). Introducing diversity among the models of multi-label classification ensemble.. The European Symposium on Artificial Neural Networks. 1 indexed citations
15.
Arazy, Ofer, Nanda Kumar, & Bracha Shapira. (2009). A Theory-Driven Design Framework for Social Recommender Systems. Journal of the Association for Information Systems. 14 indexed citations
16.
Raghupathi, Viju, Ofer Arazy, Nanda Kumar, & Bracha Shapira. (2008). Opinion Leadership: Non-Work-Related Advice in a Work Setting. Journal of electronic commerce research. 10(4). 220. 8 indexed citations
17.
Arazy, Ofer, Nanda Kumar, & Bracha Shapira. (2008). Improving Social Recommender Systems. SSRN Electronic Journal.
18.
Boger, Zvi, Tsvi Kuflik, Bracha Shapira, & Peretz Shoval. (2000). Information Filtering and Automatic Keyword Identification by Artificial Neural Networks. Journal of the Association for Information Systems. 379–385. 1 indexed citations
19.
Frisch, Amos, Elena Michaelovsky, Rivka Rockah, et al.. (2000). Association between obsessive-compulsive disorder and polymorphisms of genes encoding components of the serotonergic and dopaminergic pathways. European Neuropsychopharmacology. 10(3). 205–209. 99 indexed citations
20.
Kohn, Yoav, Richard P. Ebstein, Uriel Heresco‐Levy, et al.. (1997). Dopamine D4 receptor gene polymorphisms: relation to ethnicity, no association with schizophrenia and response to clozapine in Israeli subjects. European Neuropsychopharmacology. 7(1). 39–43. 62 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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