What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
Giovanni Semeraro spends much of his time researching Artificial intelligence, Recommender system, Information retrieval, Natural language processing and World Wide Web.
His Artificial intelligence research includes elements of Concept learning, Machine learning, Structure and Algorithm.
His work on Collaborative filtering as part of general Recommender system study is frequently connected to Serendipity, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The study incorporates disciplines such as User modeling, Metadata, User profile, Exploit and Profiling in addition to Information retrieval.
His User profile research is multidisciplinary, incorporating perspectives in Semantics and User-generated content.
His research in World Wide Web focuses on subjects like Context, which are connected to Personalization.
His most cited work include:
- Content-based Recommender Systems: State of the Art and Trends (1097 citations)
- A comparative analysis of methods for pruning decision trees (411 citations)
- A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation (130 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Recommender system, Artificial intelligence, Information retrieval, World Wide Web and Natural language processing.
Particularly relevant to Collaborative filtering is his body of work in Recommender system.
His research integrates issues of Exploit, Machine learning and Data mining in his study of Artificial intelligence.
His work deals with themes such as Semantics and Context, which intersect with Information retrieval.
His Personalization, E-commerce and User-generated content study in the realm of World Wide Web connects with subjects such as Digital library.
His biological study spans a wide range of topics, including Word-sense disambiguation, Word, SemEval and Random indexing.
He most often published in these fields:
- Recommender system (34.20%)
- Artificial intelligence (32.54%)
- Information retrieval (30.88%)
What were the highlights of his more recent work (between 2015-2021)?
- Recommender system (34.20%)
- Artificial intelligence (32.54%)
- Information retrieval (30.88%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Recommender system, Artificial intelligence, Information retrieval, Natural language processing and World Wide Web.
The Recommender system study combines topics in areas such as Linked data, User profile, Natural language and Human–computer interaction.
His study connects Machine learning and Artificial intelligence.
His study focuses on the intersection of Information retrieval and fields such as Representation with connections in the field of Service.
His research investigates the connection between Natural language processing and topics such as Distributional semantics that intersect with problems in Context.
Social media is the focus of his World Wide Web research.
Between 2015 and 2021, his most popular works were:
- Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems (60 citations)
- AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets. (53 citations)
- Centroid-based Text Summarization through Compositionality of Word Embeddings (52 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
The scientist’s investigation covers issues in Recommender system, Artificial intelligence, Information retrieval, Natural language processing and Natural language.
Recommender system is the subject of his research, which falls under World Wide Web.
His Artificial intelligence study frequently intersects with other fields, such as Machine learning.
His Information retrieval study incorporates themes from Exploit, Sentiment analysis, Process and Granularity.
His Natural language processing study combines topics from a wide range of disciplines, such as Diachronic analysis, Word, The Internet and Semantic change.
His Natural language research is multidisciplinary, incorporating elements of Domain, Social network, Chatbot, Sentence and Alpha.
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