Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Personalized, interactive news on the Web
Multimedia Systems
A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Evaluating adaptive user profiles for news classification
Proceedings of the 9th international conference on Intelligent user interfaces
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Hermes: a semantic web-based news decision support system
Proceedings of the 2008 ACM symposium on Applied computing
Ontology-Based Personalised and Context-Aware Recommendations of News Items
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
ICWE '9 Proceedings of the 9th International Conference on Web Engineering
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Ontology-based news recommendation
Proceedings of the 2010 EDBT/ICDT Workshops
Semantics-based news recommendation
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Hi-index | 0.00 |
Content-based news recommendations are usually made by employing the cosine similarity and the TF-IDF weighting scheme for terms occurring in news messages and user profiles. Recent developments, such as SF-IDF, have elevated news recommendation to a new level of abstraction by additionally taking into account term meaning through the exploitation of synsets from semantic lexicons and the cosine similarity. Other state-of-the-art semantic recommenders, like SS, make use of semantic lexicon-driven similarities. A shortcoming of current semantic recommenders is that they do not take into account the various semantic relationships between synsets, providing only for a limited understanding of news semantics. Therefore, we extend the SF-IDF weighting technique by additionally considering the synset semantic relationships from a semantic lexicon. The proposed recommendation method, SF-IDF+, as well as SF-IDF and several semantic similarity lexicon-driven methods have been implemented in Ceryx, an extension to the Hermes news personalization service. An evaluation on a data set containing financial news messages shows that overall (by accounting for all considered cut-off values) SF-IDF+ outperforms TF-IDF, SS, and SF-IDF in terms of F1-scores.