What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Information retrieval
Tetsuya Sakai focuses on Information retrieval, Relevance, Data mining, Discriminative model and Information access.
His biological study spans a wide range of topics, including Test and World Wide Web.
His Relevance study combines topics in areas such as Question answering and Selection.
His Data mining research is multidisciplinary, relying on both Statistical hypothesis testing, Discounted cumulative gain and Rank correlation.
His Discriminative model research includes elements of Ranking, Learning to rank and Web page.
His Information access research focuses on subjects like Data science, which are linked to Automatic summarization.
His most cited work include:
- Evaluating evaluation metrics based on the bootstrap (171 citations)
- Information filtering apparatus for selecting predetermined article from plural articles to present selected article to user, and method therefore (130 citations)
- Evaluating diversified search results using per-intent graded relevance (124 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Information retrieval, Relevance, Artificial intelligence, Test and Data mining.
In his articles, he combines various disciplines, including Information retrieval and Rank.
Tetsuya Sakai combines subjects such as Ranking, Contrast and Discriminative model with his study of Relevance.
His Artificial intelligence research includes themes of Machine learning, Conversation, Speech recognition and Natural language processing.
Analysis of variance is closely connected to Sample size determination in his research, which is encompassed under the umbrella topic of Test.
His Data mining study frequently draws connections to adjacent fields such as Discounted cumulative gain.
He most often published in these fields:
- Information retrieval (48.11%)
- Relevance (23.11%)
- Artificial intelligence (18.18%)
What were the highlights of his more recent work (between 2018-2021)?
- Information retrieval (48.11%)
- Relevance (23.11%)
- Artificial intelligence (18.18%)
In recent papers he was focusing on the following fields of study:
Tetsuya Sakai spends much of his time researching Information retrieval, Relevance, Artificial intelligence, Test and Natural language processing.
The various areas that Tetsuya Sakai examines in his Information retrieval study include Data warehouse and Data set.
His Relevance research incorporates elements of Crowdsourcing, Learning to rank, Preference and Presentation.
His work on Natural language generation as part of general Artificial intelligence research is often related to Lyrics, thus linking different fields of science.
His Test study integrates concerns from other disciplines, such as Analysis of variance, Sample size determination, Replication, Statistical power and Set.
His Natural language processing study incorporates themes from Domain, Poetry and Table.
Between 2018 and 2021, his most popular works were:
- Personalized Reason Generation for Explainable Song Recommendation (14 citations)
- The SIGIR 2019 Open-Source IR Replicability Challenge (OSIRRC 2019) (8 citations)
- Which Diversity Evaluation Measures Are "Good"? (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Information retrieval, Relevance, Data science, Open source and Clef.
He connects Information retrieval with Hierarchy in his research.
His Relevance study combines topics from a wide range of disciplines, such as Personalization, Recommender system, Natural language generation, Natural language and Pattern recognition.
The study incorporates disciplines such as Context and Generalizability theory in addition to Clef.
Tetsuya Sakai focuses mostly in the field of Replication, narrowing it down to matters related to Test and, in some cases, Software engineering, Set and Section.
His Preference research is multidisciplinary, incorporating perspectives in Learning to rank, Pairwise comparison and Data set.
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