Di Buccio, Emanuelle; Melucci, Massimo and Song, Dawei
(2010).
|
PDF (Version of Record)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (292kB) | Preview |
| URL: | http://research.microsoft.com/en-us/um/people/ryen... |
|---|---|
| Google Scholar: | Look up in Google Scholar |
Abstract
The behavior of the user when interacting with a result page or the corresponding landing documents is a possible source of evidence that Information Retrieval (IR) systems can ex- ploit to assist the user when searching for information. In- teraction features can be adopted as evidence to model the user behavior, thus making it usable to assist relevance pre- diction. One issue when dealing with interaction features is the selection of the sources from which these features are distilled. Individual users and group of users which perform a similar task or look for information matching the same query are possible sources. This paper will focus on these two sources, particularly investigating group of users searching for the same topic as source for interaction features to be used as an alternative to, or in combination with, indi- vidual users. The objective of this work is to investigate the impact of diverse combinations of these sources on the retrieval effectiveness, specifically when interaction features are used as evidence to support document re-ranking.
| Item Type: | Conference or Workshop Item |
|---|---|
| Copyright Holders: | 2010 The Authors |
| Extra Information: | pp.63-66 |
| Keywords: | interaction features; implicit relevance feedback |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Related URLs: | |
| Item ID: | 35129 |
| Depositing User: | Dawei Song |
| Date Deposited: | 01 Nov 2012 11:43 |
| Last Modified: | 15 Dec 2016 18:10 |
| URI: | http://oro.open.ac.uk/id/eprint/35129 |
| Share this page: |     |
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.