Bracha Shapira mostly deals with Recommender system, Artificial intelligence, World Wide Web, Machine learning and Collaborative filtering. Her studies in Recommender system integrate themes in fields like Multimedia, Data science and Design science. Her Multimedia research is multidisciplinary, incorporating elements of Information technology and Information overload.
Her research investigates the connection between Information overload and topics such as User interface that intersect with issues in Variety. Her World Wide Web research incorporates elements of Software and Service. Her Machine learning research includes elements of Context and Data mining.
The scientist’s investigation covers issues in Recommender system, Artificial intelligence, Data mining, World Wide Web and Information retrieval. Her Recommender system research is multidisciplinary, incorporating perspectives in Event, RSS and Data science. Bracha Shapira has included themes like Context, Machine learning and Natural language processing in her Artificial intelligence study.
Her Data mining research is multidisciplinary, relying on both User profile and Cluster analysis. Bracha Shapira interconnects Computer security, Multimedia and Service in the investigation of issues within World Wide Web. Her work on Ranking and Relevance as part of general Information retrieval research is often related to Content, thus linking different fields of science.
Her primary areas of study are Artificial intelligence, Machine learning, Recommender system, Context and Process. Her work deals with themes such as Identification and Natural language processing, which intersect with Artificial intelligence. When carried out as part of a general Machine learning research project, her work on Ensemble learning and Feature is frequently linked to work in Post hoc and Simple, therefore connecting diverse disciplines of study.
Her work carried out in the field of Recommender system brings together such families of science as Event and Database activity monitoring. Her Context study integrates concerns from other disciplines, such as Representation, Recurrent neural network, Code and Machine translation. As a part of the same scientific family, Bracha Shapira mostly works in the field of Deep learning, focusing on Artificial neural network and, on occasion, Data mining and Adjacency matrix.
Bracha Shapira spends much of her time researching Artificial intelligence, Machine learning, Context, Information retrieval and Collaborative filtering. Bracha Shapira has researched Artificial intelligence in several fields, including Natural language processing and Identification. A large part of her Machine learning studies is devoted to Recommender system.
Her research integrates issues of Ensemble learning, Mobile computing, Mobile device and Cluster analysis in her study of Recommender system. The various areas that Bracha Shapira examines in her Information retrieval study include Identity, Code and Personalization. Her Collaborative filtering study combines topics in areas such as Space, State and Curse of dimensionality.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Introduction to Recommender Systems Handbook
Francesco Ricci;Lior Rokach;Bracha Shapira.
Recommender Systems Handbook (2011)
Recommender Systems Handbook
Francesco Ricci;Lior Rokach;Bracha Shapira;Paul B. Kantor.
Recommender Systems: Introduction and Challenges
Francesco Ricci;Lior Rokach;Bracha Shapira.
Recommender Systems Handbook (2015)
Information Filtering: Overview of Issues, Research and Systems
Uri Hanani;Bracha Shapira;Peretz Shoval.
User Modeling and User-adapted Interaction (2001)
Mobile malware detection through analysis of deviations in application network behavior
Asaf Shabtai;Lena Tenenboim-Chekina;Dudu Mimran;Lior Rokach.
Computers & Security (2014)
Facebook single and cross domain data for recommendation systems
Bracha Shapira;Lior Rokach;Shirley Freilikhman.
User Modeling and User-adapted Interaction (2013)
A Theory-Driven Design Framework for Social Recommender Systems
Ofer Arazy;Nanda Kumar;Bracha Shapira.
Journal of the Association for Information Systems (2010)
Improving Social Recommender Systems
O. Arazy;N. Kumar;B. Shapira.
IT Professional (2009)
Efficient Multidimensional Suppression for K-Anonymity
S. Kisilevich;L. Rokach;Y. Elovici;B. Shapira.
IEEE Transactions on Knowledge and Data Engineering (2010)
Unknown malware detection using network traffic classification
Dmitri Bekerman;Bracha Shapira;Lior Rokach;Ariel Bar.
communications and networking symposium (2015)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: