SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships
In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it combines four important factors in scientific publications: content, publicity, impact and randomness. Particularly, a recommendation system (called SciRecSys) has been presented to support users to efficiently find out relevant articles.
KeywordsKeyword ranking Keyword similarity Keyword inference Scientific Recommendation System Bibliographical corpus
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- 2.Bollegala, D., Matsuo, Y., Ishizuka, M.: Measuring semantic similarity between words using web search engines. In: Williamson, C.L., Zurko, M.E., Patel-Schneider, P.F., Shenoy, P.J. (eds.) Proceedings of the 16th International Conference on World Wide Web (WWW 2007), Banff, Alberta, Canada, May 8-12, pp. 757–766. ACM (2007)Google Scholar
- 6.Huang, A.: Similarity measures for text document clustering. In: Proceedings of 6th In New Zealand Computer Science Research Student Conference, Christchurch, New Zealand, pp. 49–56 (2008)Google Scholar
- 11.Schickel-Zuber, V., Faltings, B.: Oss: A semantic similarity function based on hierarchical ontologies. In: Veloso, M.M. (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India, January 6-12, pp. 551–556 (2007)Google Scholar