2017 - Royal Netherlands Academy of Arts and Sciences
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.
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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Patrick Blackburn;Maarten de Rijke;Yde Venema.
Using WordNet to measure semantic orientations of adjectives
Jaap Kamps;Maarten Marx;Robert J. Mokken;Maarten de Rijke.
language resources and evaluation (2004)
Formal models for expert finding in enterprise corpora
Krisztian Balog;Leif Azzopardi;Maarten de Rijke.
international acm sigir conference on research and development in information retrieval (2006)
Overview of the TREC 2006 Blog Track
Iadh Ounis;Craig Macdonald;Maarten de Rijke;Gilad Mishne.
text retrieval conference (2006)
Short Text Similarity with Word Embeddings
Tom Kenter;Maarten de Rijke.
conference on information and knowledge management (2015)
Adding semantics to microblog posts
Edgar Meij;Wouter Weerkamp;Maarten de Rijke.
web search and data mining (2012)
Accessing Multilingual Information Repositories
Carol Peters;Fredric C. Gey;Julio Gonzalo;Henning Müller.
Krisztian Balog;Yi Fang;Maarten de Rijke;Pavel Serdyukov.
ENSM-SE at CLEF 2006 : Fuzzy Proximity Method with an Adhoc Influence Function in Evaluation of Multilingual and Multi-modal Information Retrieval 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain
Carol Peters;Paul Clough;Fredric C. Gey;Jussi Karlgren.
Lecture Notes in Computer Science (2007)
Click Models for Web Search
Aleksandr Chuklin;Ilya Markov;Maarten de Rijke.
Profile was last updated on December 6th, 2021.
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