What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Machine learning
His main research concerns Data mining, Information retrieval, Set, Task and Artificial intelligence.
His work deals with themes such as Algorithm, State, Scalability and Representation, which intersect with Data mining.
He combines subjects such as Content-based image retrieval, Image retrieval and Cluster analysis with his study of Information retrieval.
His Set research is multidisciplinary, relying on both Recommender system, World Wide Web, Point of interest and Web query classification.
His research in Task focuses on subjects like Knowledge base, which are connected to Relevance.
His study focuses on the intersection of Artificial intelligence and fields such as Machine learning with connections in the field of Tree traversal, Tree and Rank.
His most cited work include:
- Fast and memory efficient mining of frequent closed itemsets (192 citations)
- Boosting the performance of Web search engines: Caching and prefetching query results by exploiting historical usage data (190 citations)
- A hybrid heuristic for the traveling salesman problem (160 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Information retrieval, Data mining, Artificial intelligence, Search engine and Parallel computing.
He focuses mostly in the field of Information retrieval, narrowing it down to matters related to Image retrieval and, in some cases, Nearest neighbor search.
His study on Data mining also encompasses disciplines like
- Set that connect with fields like Algorithm,
- Scalability that connect with fields like Parallel algorithm.
His Artificial intelligence study integrates concerns from other disciplines, such as Ranking, Machine learning, Task and Natural language processing.
His Search engine research incorporates elements of Inverted index and Cache.
His Query expansion study combines topics in areas such as Sargable, Query language and Query optimization.
He most often published in these fields:
- Information retrieval (25.65%)
- Data mining (24.78%)
- Artificial intelligence (18.26%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (18.26%)
- Information retrieval (25.65%)
- Ranking (7.83%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Information retrieval, Ranking, Machine learning and Search engine.
His research in Artificial intelligence tackles topics such as Natural language processing which are related to areas like Ambiguity.
His work on Recommender system, Question answering and Search engine indexing as part of his general Information retrieval study is frequently connected to Transformer, thereby bridging the divide between different branches of science.
His Search engine indexing research focuses on subjects like Trie, which are linked to Data mining.
His Data mining research incorporates themes from Capacity planning and Cache algorithms.
The Search engine study combines topics in areas such as Scheme, Query expansion, Reduction and Cache.
Between 2017 and 2021, his most popular works were:
- Efficient Document Re-Ranking for Transformers by Precomputing Term Representations (17 citations)
- Expansion via Prediction of Importance with Contextualization (15 citations)
- Boosting Ride Sharing With Alternative Destinations (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Information retrieval, Recommender system, Data science and Natural language processing.
His research ties Machine learning and Artificial intelligence together.
The concepts of his Machine learning study are interwoven with issues in Tree traversal, Interleaving and Data structure.
Raffaele Perego works in the field of Information retrieval, namely Question answering.
The study incorporates disciplines such as Predictive modelling and Data warehouse in addition to Recommender system.
His Natural language study, which is part of a larger body of work in Natural language processing, is frequently linked to Sequence, bridging the gap between disciplines.
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