Bamshad Mobasher mostly deals with Data mining, Web mining, Recommender system, Personalization and Cluster analysis. His biological study spans a wide range of topics, including Web intelligence, Web modeling and Data Web. His Data Web research is multidisciplinary, relying on both Social Semantic Web and Web navigation.
His Collaborative filtering study in the realm of Recommender system connects with subjects such as Domain analysis. His Personalization research incorporates elements of Information retrieval and Usage data. His biological study deals with issues like Process, which deal with fields such as Task and Set.
Recommender system, World Wide Web, Information retrieval, Personalization and Collaborative filtering are his primary areas of study. The concepts of his Recommender system study are interwoven with issues in Task, Data mining and Artificial intelligence. Bamshad Mobasher focuses mostly in the field of World Wide Web, narrowing it down to matters related to Knowledge extraction and, in some cases, Web usage analysis.
His Information retrieval research is multidisciplinary, incorporating elements of Annotation, Calibration, Cluster analysis, User profile and Preference. Bamshad Mobasher usually deals with Personalization and limits it to topics linked to User modeling and Human–computer interaction. His Web mining study integrates concerns from other disciplines, such as Web analytics, Web intelligence, Web modeling, Social Semantic Web and Data Web.
His main research concerns Recommender system, Popularity, Information retrieval, Artificial intelligence and Machine learning. His Recommender system study combines topics in areas such as Calibration, Ranking, Aggregate and Data science. As part of one scientific family, Bamshad Mobasher deals mainly with the area of Information retrieval, narrowing it down to issues related to the Graph, and often Set.
His work deals with themes such as Matching and Task, which intersect with Artificial intelligence. As a part of the same scientific study, Bamshad Mobasher usually deals with the Machine learning, concentrating on Variety and frequently concerns with SIMPLE. Personalization is the focus of his World Wide Web research.
His primary scientific interests are in Recommender system, Popularity, Information retrieval, Representation and Machine learning. The Collaborative filtering research Bamshad Mobasher does as part of his general Recommender system study is frequently linked to other disciplines of science, such as Transparency, therefore creating a link between diverse domains of science. Many of his studies on Information retrieval involve topics that are commonly interrelated, such as Graph.
The study incorporates disciplines such as Quality, Feedback loop and Artificial intelligence in addition to Machine learning. His research integrates issues of Range and Preference in his study of Calibration. His Aggregate study incorporates themes from Maximum flow problem, Set and Graph based.
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Data Preparation for Mining World Wide Web Browsing Patterns
Robert Cooley;Bamshad Mobasher;Jaideep Srivastava.
Knowledge and Information Systems (1999)
Context-Aware Recommender Systems
Gediminas Adomavicius;Bamshad Mobasher;Francesco Ricci;Alexander Tuzhilin.
Ai Magazine (2011)
Web mining: information and pattern discovery on the World Wide Web
R. Cooley;B. Mobasher;J. Srivastava.
international conference on tools with artificial intelligence (1997)
Automatic personalization based on Web usage mining
Bamshad Mobasher;Robert Cooley;Jaideep Srivastava.
Communications of The ACM (2000)
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Bamshad Mobasher;Honghua Dai;Tao Luo;Miki Nakagawa.
Data Mining and Knowledge Discovery (2002)
Personalized recommendation in social tagging systems using hierarchical clustering
Andriy Shepitsen;Jonathan Gemmell;Bamshad Mobasher;Robin Burke.
conference on recommender systems (2008)
Effective personalization based on association rule discovery from web usage data
Bamshad Mobasher;Honghua Dai;Tao Luo;Miki Nakagawa.
web information and data management (2001)
Data mining for web personalization
The adaptive web (2007)
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
Bamshad Mobasher;Robin Burke;Runa Bhaumik;Chad Williams.
ACM Transactions on Internet Technology (2007)
Web search personalization with ontological user profiles
Ahu Sieg;Bamshad Mobasher;Robin Burke.
conference on information and knowledge management (2007)
Profile was last updated on December 6th, 2021.
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