What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- World Wide Web
Barry Smyth mainly focuses on World Wide Web, Recommender system, Case-based reasoning, Information retrieval and Artificial intelligence.
His study in World Wide Web is interdisciplinary in nature, drawing from both Multimedia, Service and Information system.
Particularly relevant to Collaborative filtering is his body of work in Recommender system.
His work carried out in the field of Case-based reasoning brings together such families of science as Risk analysis, Software design, Reasoning system and Competence.
His Information retrieval research incorporates elements of Ranking, User-generated content, Popularity and Real-time web.
In his study, Context is inextricably linked to Machine learning, which falls within the broad field of Artificial intelligence.
His most cited work include:
- Trust in recommender systems (748 citations)
- Retrieval, reuse, revision and retention in case-based reasoning (458 citations)
- Recommending twitter users to follow using content and collaborative filtering approaches (406 citations)
What are the main themes of his work throughout his whole career to date?
World Wide Web, Recommender system, Information retrieval, Artificial intelligence and Case-based reasoning are his primary areas of study.
His study on World Wide Web is mostly dedicated to connecting different topics, such as Multimedia.
He works on Recommender system which deals in particular with Collaborative filtering.
His Information retrieval research includes themes of Service and Profiling.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Competence.
His biological study spans a wide range of topics, including Adaptation, Process and Reasoning system.
He most often published in these fields:
- World Wide Web (44.99%)
- Recommender system (33.79%)
- Information retrieval (20.24%)
What were the highlights of his more recent work (between 2014-2021)?
- Recommender system (33.79%)
- World Wide Web (44.99%)
- Data science (6.29%)
In recent papers he was focusing on the following fields of study:
Barry Smyth focuses on Recommender system, World Wide Web, Data science, Information retrieval and Artificial intelligence.
He is involved in the study of Recommender system that focuses on Collaborative filtering in particular.
In the field of World Wide Web, his study on Crowdsourcing overlaps with subjects such as Preference data.
As a part of the same scientific study, Barry Smyth usually deals with the Data science, concentrating on Process and frequently concerns with Ranking.
His Artificial intelligence study frequently draws connections between adjacent fields such as Machine learning.
His study in Case-based reasoning is interdisciplinary in nature, drawing from both Marathon running and Feature.
Between 2014 and 2021, his most popular works were:
- Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews (78 citations)
- Combining similarity and sentiment in opinion mining for product recommendation (43 citations)
- Why I like it: multi-task learning for recommendation and explanation (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- World Wide Web
Barry Smyth mainly investigates Recommender system, World Wide Web, Sentiment analysis, Artificial intelligence and Race.
His Recommender system study results in a more complete grasp of Information retrieval.
His work deals with themes such as User experience design and Feature, which intersect with World Wide Web.
In Sentiment analysis, Barry Smyth works on issues like Context, which are connected to Ranking.
His Artificial intelligence research incorporates themes from Machine learning and Collaborative filtering.
His research in Knowledge representation and reasoning focuses on subjects like Case-based reasoning, which are connected to Task and Operations research.
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.
