ACM Home Page
Please provide us with feedback. Feedback
Activity put in context: identifying implicit task context within the user's document interaction
Full text PdfPdf (443 KB)
Source ACM International Conference Proceeding Series; Vol. 348 archive
Proceedings of the second international symposium on Information interaction in context table of contents
London, United Kingdom
SESSION: Personalisation table of contents
Pages 51-56  
Year of Publication: 2008
ISBN:978-1-60558-310-5
Authors
Karl Gyllstrom  UNC-Chapel Hill / HP Labs
Craig Soules  HP Labs
Alistair Veitch  HP Labs
Sponsors
: Yahoo! Research
: Information Retrieval Facility
ACM: Association for Computing Machinery
British Computer Society : BCS
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 5,   Downloads (12 Months): 91,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1414694.1414707
What is a DOI?

ABSTRACT

Modern desktop search is ill-fitted to our personal document workspace. On one hand, many of the methods which render web search effective cannot be applied on the desktop. On the other, desktop search does not take full advantage of attributes that are unique to our personal documents. In this work, we present Confluence, a desktop search system that addresses this problem by capturing the task context within which a user interacts with their documents. This context is then integrated with traditional desktop search techniques to enable task-based document retrieval.

Building upon Connections, a system that identifies task context by passively monitoring the user's interaction with their documents within the file system. Confluence also traces user activity within the user interface and incorporates methods to analyze and integrate this new stream of information. We show that this approach significantly improves the accuracy of task identification, achieving 25% to 30% better recall.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
2
3
4
5
 
6
D. Karger, K. Bakshi, D. Huynh, D. Quan, and V. Sinha. Haystack: A general purpose information management tool for end users of semistructured data. In CIDR, pages 13--26, 2005.
7
8
9
10
11
12


Collaborative Colleagues:
Karl Gyllstrom: colleagues
Craig Soules: colleagues
Alistair Veitch: colleagues