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 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.
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.
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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Content-based Recommender Systems: State of the Art and Trends
Pasquale Lops;Marco de Gemmis;Giovanni Semeraro.
Recommender Systems Handbook (2011)
A comparative analysis of methods for pruning decision trees
F. Esposito;D. Malerba;G. Semeraro;J. Kay.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)
A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation
Marco Degemmis;Pasquale Lops;Giovanni Semeraro.
User Modeling and User-adapted Interaction (2007)
Semantics-Aware Content-Based Recommender Systems
Marco de Gemmis;Pasquale Lops;Cataldo Musto;Fedelucio Narducci.
conference on recommender systems (2015)
Introducing Serendipity in a Content-Based Recommender System
L. Iaquinta;M. de Gemmis;P. Lops;G. Semeraro.
international conference hybrid intelligent systems (2008)
Integrating tags in a semantic content-based recommender
Marco de Gemmis;Pasquale Lops;Giovanni Semeraro;Pierpaolo Basile.
conference on recommender systems (2008)
A logic framework for the incremental inductive synthesis of Datalog theories
G. Semeraro;F. Esposito;D. Malerba;N. Fanizzi.
Lecture Notes in Computer Science (1998)
An Enhanced Lesk Word Sense Disambiguation Algorithm through a Distributional Semantic Model
Pierpaolo Basile;Annalina Caputo;Giovanni Semeraro.
international conference on computational linguistics (2014)
A Comparison of Lexicon-based Approaches for Sentiment Analysis of Microblog Posts.
Cataldo Musto;Giovanni Semeraro;Marco Polignano.
MULTISTRATEGY LEARNING FOR DOCUMENT RECOGNITION
Floriana Esposito;Donato Malerba;Giovanni Semeraro.
Applied Artificial Intelligence (1994)
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