What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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.
His most cited work include:
- Data Preparation for Mining World Wide Web Browsing Patterns (1437 citations)
- Context-Aware Recommender Systems (1238 citations)
- Automatic personalization based on Web usage mining (1219 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Recommender system (56.54%)
- World Wide Web (35.38%)
- Information retrieval (28.08%)
What were the highlights of his more recent work (between 2018-2021)?
- Recommender system (56.54%)
- Popularity (6.54%)
- Information retrieval (28.08%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- Managing Popularity Bias in Recommender Systems with Personalized Re-ranking (31 citations)
- Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. (23 citations)
- The Unfairness of Popularity Bias in Recommendation (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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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