L
Leandro Krug Wives
Researcher at Universidade Federal do Rio Grande do Sul
Publications - 115
Citations - 1001
Leandro Krug Wives is an academic researcher from Universidade Federal do Rio Grande do Sul. The author has contributed to research in topics: Recommender system & Context (language use). The author has an hindex of 15, co-authored 115 publications receiving 921 citations. Previous affiliations of Leandro Krug Wives include Universidade Feevale.
Papers
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Journal ArticleDOI
Concept-based knowledge discovery in texts extracted from the Web
TL;DR: The approach is based on concepts, which are extracted from texts to be used as characteristics in the mining process, and statistical techniques are applied on concepts in order to find interesting patterns in concept distributions or associations.
Journal ArticleDOI
LinkedWS: A Novel Web Services Discovery Model Based on the Metaphor of Social Networks
TL;DR: LinkedWS is a social networks discovery model to capture the different interactions that occur between Web services, inspired by the conventional human social networks on the net, like Facebook and Twitter.
Book ChapterDOI
Collaboration recommendation on academic social networks
TL;DR: The architecture for such approach and the metrics involved in recommending collaborations on the context of academic Social Networks are introduced and an initial case study is presented to validate the approach.
Journal ArticleDOI
Using Social Networks for Web Services Discovery
Zakaria Maamar,Pedro Santos,Leandro Krug Wives,Youakim Badr,Noura Faci,José Palazzo Moreira de Oliveira +5 more
TL;DR: The authors describe how service engineers can capitalize on Web services' interactions - namely, collaboration, substitution, and competition - to build social networks for service discovery.
Journal Article
Examining Multiple Features for Author Profiling
Edson R. D. Weren,Anderson Uilian Kauer,Lucas E. P. Mizusaki,Viviane Pereira Moreira,J. Palazzo M. de Oliveira,Leandro Krug Wives +5 more
TL;DR: Experiments show that a classifier using the features explored here have outperformed the state-of-the art and the experiments show that the Information Retrieval features proposed in this work are the most discriminative and yield the best class predictions.