Abstract
In a distributed information retrieval system, how to merge results from different text databases is an important issue, since it affects the effectiveness of the result considerably. In many cases, the underlining systems only provide a ranked list of documents for any information need. In this paper, we investigate the relation between rank and relevance in resultant document lists, and find that the cubic model is a good option for this. Extensive experimentation is conducted to evaluate the performance of the cubic model for results merging. The experimental results demonstrate that the cubic model is better than the logistic model, which was suggested by a previous research.
| Original language | English |
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| Title of host publication | Unknown Host Publication |
| Publisher | IEEE Xplore |
| Pages | 3-10 |
| Number of pages | 8 |
| ISBN (Print) | 978-0-7695-3810-5 |
| DOIs | |
| Publication status | Published - 31 Dec 2009 |
| Event | Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on - Zhuhai, China Duration: 31 Dec 2009 → … |
Conference
| Conference | Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on |
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| Period | 31/12/09 → … |
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Cite this
Wu, S., Bi, Y., & Liu, J. (2009). The Cubic Regression Model for Merging Results from Multiple Text Databases. In Unknown Host Publication (pp. 3-10). IEEE Xplore. https://doi.org/10.1109/SKG.2009.100