What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Information retrieval, Artificial intelligence, Natural language processing, Machine learning and World Wide Web.
His biological study spans a wide range of topics, including Language model, Social media and Rank.
In his work, Baseline is strongly intertwined with Set, which is a subfield of Artificial intelligence.
His work in Natural language processing addresses subjects such as Clef, which are connected to disciplines such as Search engine indexing.
The study incorporates disciplines such as Intranet, Information needs, Structure, Pairwise comparison and Session in addition to Machine learning.
His World Wide Web study incorporates themes from Feature, Key and Reputation management.
His most cited work include:
- Modal Logic (2603 citations)
- Using WordNet to measure semantic orientations of adjectives (585 citations)
- Formal models for expert finding in enterprise corpora (507 citations)
What are the main themes of his work throughout his whole career to date?
Maarten de Rijke mainly investigates Information retrieval, Artificial intelligence, Natural language processing, Machine learning and World Wide Web.
His study looks at the relationship between Information retrieval and fields such as Ranking, as well as how they intersect with chemical problems.
He frequently studies issues relating to Context and Artificial intelligence.
His study in Natural language processing is interdisciplinary in nature, drawing from both Clef and Multilingualism.
His Learning to rank research is multidisciplinary, incorporating perspectives in Counterfactual thinking and Rank.
His Query expansion research includes themes of Web search query and Query optimization.
He most often published in these fields:
- Information retrieval (42.91%)
- Artificial intelligence (35.30%)
- Natural language processing (16.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (35.30%)
- Information retrieval (42.91%)
- Machine learning (15.49%)
In recent papers he was focusing on the following fields of study:
Maarten de Rijke mainly focuses on Artificial intelligence, Information retrieval, Machine learning, Counterfactual thinking and Learning to rank.
His work carried out in the field of Artificial intelligence brings together such families of science as Context and Natural language processing.
His Natural language processing research is multidisciplinary, relying on both Semantics and Conversation.
His research in Information retrieval tackles topics such as Session which are related to areas like Preference.
The concepts of his Machine learning study are interwoven with issues in Bayesian probability and Component.
His work in Learning to rank addresses issues such as Rank, which are connected to fields such as Regret.
Between 2018 and 2021, his most popular works were:
- RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation (52 citations)
- A Collaborative Session-based Recommendation Approach with Parallel Memory Modules (43 citations)
- To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Information retrieval, Recommender system and Learning to rank.
His research integrates issues of Generator, Conversation and Natural language processing in his study of Artificial intelligence.
His Information retrieval study combines topics in areas such as Context and Information needs.
His Recommender system research incorporates themes from Matching and Natural language.
His Learning to rank study combines topics in areas such as Counterfactual thinking, Rank and Propensity score matching.
His Rank research incorporates elements of Regret and Relevance.
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