Temporal filtering system to reduce the risk of spoiling a user's enjoyment

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Temporal filtering system to reduce the risk of spoiling a user's enjoyment

Full text

Pdf (528 KB)

Source

International Conference on Intelligent User Interfaces archive
Proceedings of the 12th international conference on Intelligent user interfaces table of contents

Honolulu, Hawaii, USA

SESSION: Short papers table of contents

Pages: 345 - 348

Year of Publication: 2007

ISBN:1-59593-481-2

Authors

Satoshi Nakamura	Kyoto University, Kyoto, Japan
Katsumi Tanaka	Kyoto University, Kyoto, Japan

Sponsors

ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 10, Downloads (12 Months): 43, Citation Count: 1

Additional Information:

abstract references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1216295.1216363 What is a DOI?

ABSTRACT

This paper proposes a temporal filtering system called the Anti-Spoiler system. The system changes filters dynamically based on user-specified preferences and the user's timetable. The system then blocks contents that would spoil the user's enjoyment of a previously unwatched content. The system analyzes a user-requested Web content, and then uses filters to prevent portions of the content being displayed that might spoil user's enjoyment. For example, the system hides the final score of football from the Web content before watching it on TV.

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	Symantec Corp., Norton Internet Security, http://www.symantec.com/.
	2	McAfee Inc., Virus Scan, http://www.mcafee.com/.
	3	DigitalArts Inc., i-FILTER, http://www.daj.co.jp/en/ir/.
	4	Sahami, M., Dumais, S., Heckerman, D. and Horvitz EM., A Bayesian Approach to Filtering Junk Email. AAAI Workshop on Learning for Text Categorization, July 1998, AAAI Technical Report WS-98-05.
	5	Deng, C., Shipeng, Y., Ji-Rong, and Wen. Wei-Ying, Ma., "VIPS: a Vision-based Page Segmentation Algorithm", Microsoft Technical Report (2003-79), 2003.