What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- World Wide Web
His primary areas of study are World Wide Web, Social media, Internet privacy, Social web and Honeypot.
His World Wide Web research incorporates themes from Location sharing, Information retrieval and Data science.
His work deals with themes such as Text mining, Digital media, Microblogging and Login, which intersect with Information retrieval.
James Caverlee has included themes like Decision tree, Malware, Tie strength and Social system in his Social media study.
His biological study spans a wide range of topics, including Language model and Web 2.0.
The various areas that James Caverlee examines in his Social web study include Metadata, Ranking, Content management, Online community and Information sharing.
His most cited work include:
- You are where you tweet: a content-based approach to geo-locating twitter users (926 citations)
- Exploring Millions of Footprints in Location Sharing Services (585 citations)
- Uncovering social spammers: social honeypots + machine learning (516 citations)
What are the main themes of his work throughout his whole career to date?
James Caverlee mostly deals with World Wide Web, Social media, Information retrieval, Artificial intelligence and Recommender system.
His studies in Social web, Web modeling, Social Semantic Web, The Internet and Web service are all subfields of World Wide Web research.
His Social media research includes themes of Internet privacy, Precision and recall, User profile, Social system and Data science.
James Caverlee combines subjects such as Crowdsourcing, Social spam and Computer security, Honeypot with his study of Internet privacy.
His Information retrieval course of study focuses on Web page and PageRank.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Natural language processing.
He most often published in these fields:
- World Wide Web (37.84%)
- Social media (28.11%)
- Information retrieval (27.03%)
What were the highlights of his more recent work (between 2019-2021)?
- Recommender system (12.97%)
- Artificial intelligence (14.05%)
- Information retrieval (27.03%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Recommender system, Artificial intelligence, Information retrieval, Machine learning and Natural language processing.
His research in Recommender system intersects with topics in Ranking, E-commerce and Key.
The concepts of his Artificial intelligence study are interwoven with issues in Code and Identification.
In his study, James Caverlee carries out multidisciplinary Information retrieval and Style research.
His work on Relevance as part of general Machine learning study is frequently linked to Estimation, bridging the gap between disciplines.
When carried out as part of a general Natural language processing research project, his work on Unified Medical Language System is frequently linked to work in Disease, Medical diagnosis and Code, therefore connecting diverse disciplines of study.
Between 2019 and 2021, his most popular works were:
- Next-item Recommendation with Sequential Hypergraphs (14 citations)
- Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations (6 citations)
- Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
James Caverlee mainly investigates Recommender system, Artificial intelligence, Machine learning, Information retrieval and Key.
His Recommender system study combines topics in areas such as Representation, Layer, User modeling and Information sharing.
His work on Unified Medical Language System and Language model as part of general Artificial intelligence study is frequently linked to Medical diagnosis and Code, therefore connecting diverse disciplines of science.
In the subject of general Machine learning, his work in Ranking is often linked to Distribution and Mean squared error, thereby combining diverse domains of study.
His Information retrieval research is multidisciplinary, incorporating elements of Range, Continuation, Code, Consistency and Exploit.
His Key research is multidisciplinary, incorporating perspectives in Graph neural networks, Graph and Internet privacy.
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