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.
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
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.
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.
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.
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.
Fast and memory efficient mining of frequent closed itemsets
C. Lucchese;S. Orlando;R. Perego.
IEEE Transactions on Knowledge and Data Engineering (2006)
Boosting the performance of Web search engines: Caching and prefetching query results by exploiting historical usage data
Tiziano Fagni;Raffaele Perego;Fabrizio Silvestri;Salvatore Orlando.
ACM Transactions on Information Systems (2006)
A hybrid heuristic for the traveling salesman problem
R. Baraglia;J.I. Hidalgo;R. Perego.
IEEE Transactions on Evolutionary Computation (2001)
CoPhIR: a Test Collection for Content-Based Image Retrieval
Paolo Bolettieri;Andrea Esuli;Fabrizio Falchi;Claudio Lucchese.
arXiv: Multimedia (2009)
Enhancing the Apriori Algorithm for Frequent Set Counting
Salvatore Orlando;Paolo Palmerini;Raffaele Perego.
data warehousing and knowledge discovery (2001)
Identifying task-based sessions in search engine query logs
Claudio Lucchese;Salvatore Orlando;Raffaele Perego;Fabrizio Silvestri.
web search and data mining (2011)
Adaptive and resource-aware mining of frequent sets
S. Orlando;P. Palmerini;R. Perego;F. Silvestri.
international conference on data mining (2002)
Building a web-scale image similarity search system
Michal Batko;Fabrizio Falchi;Claudio Lucchese;David Novak.
Multimedia Tools and Applications (2010)
Where shall we go today?: planning touristic tours with tripbuilder
Igo Brilhante;Jose Antonio Macedo;Franco Maria Nardini;Raffaele Perego.
conference on information and knowledge management (2013)
Learning relatedness measures for entity linking
Diego Ceccarelli;Claudio Lucchese;Salvatore Orlando;Raffaele Perego.
conference on information and knowledge management (2013)
Profile was last updated on December 6th, 2021.
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University of Glasgow
University of Glasgow
Institute of Information Science and Technologies
Consiglio Nazionale delle Ricerche - CNR
Florida Institute for Human and Machine Cognition
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