What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Information retrieval
Information retrieval, Artificial intelligence, World Wide Web, Machine learning and Data mining are his primary areas of study.
In general Information retrieval, his work in Ranking and Query expansion is often linked to Diversification and Track linking many areas of study.
His research integrates issues of Ranking and Heuristics in his study of Ranking.
As a part of the same scientific family, Craig Macdonald mostly works in the field of Query expansion, focusing on Web query classification and, on occasion, Sargable and Query optimization.
He interconnects Baseline and Natural language processing in the investigation of issues within Artificial intelligence.
He has included themes like Text Retrieval Conference and Information seeking in his World Wide Web study.
His most cited work include:
- Exploiting query reformulations for web search result diversification (365 citations)
- Overview of the TREC 2006 Blog Track (246 citations)
- Terrier information retrieval platform (237 citations)
What are the main themes of his work throughout his whole career to date?
Craig Macdonald focuses on Information retrieval, Artificial intelligence, World Wide Web, Ranking and Search engine.
His Information retrieval research focuses on subjects like Social media, which are linked to Crowdsourcing.
His Artificial intelligence study combines topics in areas such as Machine learning, Recommender system and Natural language processing.
His study of Blogosphere is a part of World Wide Web.
His multidisciplinary approach integrates Ranking and Voting in his work.
His biological study deals with issues like Query optimization, which deal with fields such as Sargable.
He most often published in these fields:
- Information retrieval (55.41%)
- Artificial intelligence (20.33%)
- World Wide Web (17.70%)
What were the highlights of his more recent work (between 2017-2021)?
- Information retrieval (55.41%)
- Artificial intelligence (20.33%)
- Machine learning (13.11%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Information retrieval, Artificial intelligence, Machine learning, Recommender system and Classifier.
Learning to rank and Ranking are subfields of Information retrieval in which his conducts study.
His Artificial intelligence research is multidisciplinary, relying on both Ranking and Natural language processing.
Craig Macdonald combines subjects such as Feedback loop and Bayesian probability with his study of Machine learning.
His Recommender system research incorporates elements of Recurrent neural network and Leverage.
His studies in Classifier integrate themes in fields like Active learning, Information sensitivity, Personally identifiable information and Training set.
Between 2017 and 2021, his most popular works were:
- Using word embeddings in Twitter election classification (66 citations)
- A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation (43 citations)
- Regional Sentiment Bias in Social Media Reporting During Crises (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
His primary areas of study are Artificial intelligence, Information retrieval, Machine learning, Social media and Recommender system.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Personally identifiable information and Natural language processing.
Craig Macdonald brings together Information retrieval and Multiple days to produce work in his papers.
His work deals with themes such as Classifier, Representation, Inference and Bayesian inference, which intersect with Machine learning.
His biological study spans a wide range of topics, including Ranking, Recurrent neural network and Human–computer interaction.
Craig Macdonald has researched Ranking in several fields, including Predictive modelling, Leverage and Selection.
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