Conference proceeding
A relevance-novelty combined model for genomics search result diversification
2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 692-695
Dec 2010
Abstract
Traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents. However, this assumption may result in high redundancy and low diversity in a ranked list. In order to provide comprehensive and diverse answers to fulfill biologists' information need, we propose a relevance-novelty combined model, named RelNov model, based on the framework of an undirected graphical model. Experiments conducted on the TREC 2006 and 2007 Genomics collections show that the proposed approach is effective in promoting both diversity and relevance of retrieval ranked lists.
Metrics
11 Record Views
2 citations in Web of Science
2 citations in Scopus
Details
- Title
- A relevance-novelty combined model for genomics search result diversification
- Creators
- Xiaoshi Yin - Coll. of Comput. Sci. & Eng., Beihang Univ., Beijing, ChinaZhoujun Li - National University of Defense TechnologyJ X Huang - Sch. of Inf. Technol., York Univ., Toronto, ON, CanadaXiaohua Hu - Drexel University, Information Science
- Publication Details
- 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 692-695
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-79952435700
- Other Identifier
- 991019170359904721