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News personalization using the CF-IDF semantic recommender
- F. Goossen, W. IJntema, F. Frasincar, Frederik Hogenboom, U. Kaymak
- Computer ScienceWIMS '11
- 25 May 2011
It is demonstrated that adapting TF-IDF with the semantics of a domain ontology, resulting in Concept Frequency - Inverse Document Frequency (CF-IDs), yields better results than using the original TF- IDF method.
Semantics-based news recommendation
Two new methods based on concepts and their semantic similarities are proposed, from which the similarities between news items are derived, and test results show that SF-IDF and SS outperform the TF- IDF method on the F1-measure.
Ontology-based news recommendation
Athena, which is an extension to the existing Hermes framework, employs a user profile to store terms or concepts found in news items browsed by the user, and uses a traditional method based on TF-IDF, and several ontology-based methods to recommend new articles to the user.
A semantic approach for extracting domain taxonomies from text
A lexico-semantic pattern language for learning ontology instances from text
A Survey of event extraction methods from text for decision support systems
Domain taxonomy learning from text: The subsumption method versus hierarchical clustering
Bing-SF-IDF+: a hybrid semantics-driven news recommender
- Michel Capelle, M. Moerland, Frederik Hogenboom, F. Frasincar, Damir Vandic
- Computer ScienceSAC
- 13 April 2015
This work extends SF-IDF by also considering the synset semantic relationships, and by employing named entity similarities using Bing page counts, which outperforms TF- IDF and SF-IDs in terms of F1-scores and kappa statistics based on a news data set.
A Comparison Study for Novelty Control Mechanisms Applied to Web News Stories
- A. Verheij, A. Kleijn, F. Frasincar, Frederik Hogenboom
- Computer ScienceIEEE/WIC/ACM International Conferences on Web…
- 4 December 2012
An evaluation within the Hermes news personalization framework is performed for pair wise and non-pair wise novelty control mechanisms based on various distance measures and vector-based news representations.
News Recommendation Using Semantics with the Bing-SF-IDF Approach
This work extends SF-IDF by also employing named entity similarities using Bing page counts, and outperforms TF- IDF and its semantics-driven variants in terms of F 1-scores and kappa statistics.