What is he best known for?
The fields of study he is best known for:
- Operating system
- The Internet
- World Wide Web
His scientific interests lie mostly in Information retrieval, Data mining, Web query classification, World Wide Web and Search engine.
His studies in Web search query and Inverted index are all subfields of Information retrieval research.
His Web search query research is multidisciplinary, incorporating elements of Query language, Query expansion and Query optimization.
His Data mining study incorporates themes from Set, Heuristics and Server.
His work in the fields of World Wide Web, such as Web service, overlaps with other areas such as Display size, Usage experience and Mobile deep linking.
His Search engine research incorporates elements of Graph, Database and Cache.
His most cited work include:
- Know your neighbors: web spam detection using the web topology (306 citations)
- Boosting the performance of Web search engines: Caching and prefetching query results by exploiting historical usage data (190 citations)
- The impact of caching on search engines (180 citations)
What are the main themes of his work throughout his whole career to date?
Fabrizio Silvestri mostly deals with Information retrieval, World Wide Web, Search engine, Data mining and Web search query.
In his research, Point of interest is intimately related to Set, which falls under the overarching field of Information retrieval.
His Search engine research includes themes of Inverted index, Data structure, Database and Cache.
His research investigates the connection between Data mining and topics such as Machine learning that intersect with problems in Popularity.
His study of Web query classification is a part of Web search query.
In his work, Sargable and Query optimization is strongly intertwined with Query expansion, which is a subfield of Web query classification.
He most often published in these fields:
- Information retrieval (37.57%)
- World Wide Web (22.65%)
- Search engine (23.76%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (9.94%)
- Information retrieval (37.57%)
- World Wide Web (22.65%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Information retrieval, World Wide Web, Machine learning and Natural language processing.
His Web search query and Search engine study in the realm of Information retrieval interacts with subjects such as Intuition.
His research ties Query expansion and Web search query together.
His Search engine research is multidisciplinary, relying on both Ranking and Relevance.
His study in the fields of Web mining and Session under the domain of World Wide Web overlaps with other disciplines such as User engagement and Dwell time.
The concepts of his Machine learning study are interwoven with issues in Digital content and Data mining.
Between 2014 and 2021, his most popular works were:
- Predicting The Next App That You Are Going To Use (108 citations)
- Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search (85 citations)
- Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (80 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- The Internet
His primary areas of study are Artificial intelligence, Information retrieval, World Wide Web, Natural language processing and Machine learning.
His work on Web search query, Search engine, Ranking and Web search engine as part of general Information retrieval study is frequently linked to Order, bridging the gap between disciplines.
His study in the field of Organic search is also linked to topics like Semantic matching.
His research in Search engine intersects with topics in Query language, Query expansion and Query optimization.
His study on Search history and Personalization is often connected to Dwell time, User engagement and Display size as part of broader study in World Wide Web.
His Data mining research extends to the thematically linked field of Machine learning.
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