ACM Home Page
Please provide us with feedback. Feedback
Optimizing web traffic via the media scheduling problem
Full text MovMov (23:12),  PdfPdf (728 KB)
Source
International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
Paris, France
SESSION: Research track papers table of contents
Pages 89-98  
Year of Publication: 2009
ISBN:978-1-60558-495-9
Authors
Lars Backstrom  Cornell University, Ithaca, NY, USA
Jon Kleinberg  Cornell University, Ithaca, NY, USA
Ravi Kumar  Yahoo! Research, Sunnyvale, CA, USA
Sponsors
ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 31,   Downloads (12 Months): 116,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1557019.1557036
What is a DOI?

ABSTRACT

Website traffic varies through time in consistent and predictable ways, with highest traffic in the middle of the day. When providing media content to visitors, it is important to present repeat visitors with new content so that they keep coming back. In this paper we present an algorithm to balance the need to keep a website fresh with new content with the desire to present the best content to the most visitors at times of peak traffic. We formulate this as the media scheduling problem, where we attempt to maximize total clicks, given the overall traffic pattern and the time varying clickthrough rates of available media content. We present an efficient algorithm to perform this scheduling under certain conditions and apply this algorithm to real data obtained from server logs, showing evidence of significant improvements in traffic from our algorithmic schedules. Finally, we analyze the click data, presenting models for why and how the clickthrough rate for new content declines as it ages.


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
D. Agarwal, B. Chen, P. Elango, N. Motgi, S.-T. Park, R. Ramakrishnan, S. Roy, and J. Zachariah. Online models for content optimization. In Proc. 21st NIPS, 2008.
 
2
A.-L. Barabási. The origin of bursts and heavy tails in human dynamics. Nature, 435:207--211, 2005.
 
3
4
 
5
 
6
A. Johansen. Response time of internauts. Physica A, 296(3-4):539--546, 2000.
 
7
A. Johansen. Probing human response times. Physica A, 338(1-2):286--291, 2004.
 
8
A. Johansen and D. Sornette. Download relaxation dynamics in the WWW following newspaper publication of URL. Physica A, 276(1-2):338--345, 2000.
 
9
 
10
J. G. Oliveira and A.-L. Barabasi. Human dynamics: Darwin and Einstein correspondence patterns. Nature, 437:1251, 2005.
11
 
12
G. Szabo and B. A. Huberman. Predicting the popularity of online content. Technical Report abs/0811.0405, CoRR, 2008.
 
13
A. Vazquez. Exact results for the barabásic model of human dynamics. Phy. Rev. Let., 95:248701, 2005.
 
14
A. Vázquez, J. G. Oliveira, Z. Dezsö, K.-I. Goh, I. Kondor, and A.-L. Barabási. Modeling bursts and heavy tails in human dynamics. Phy. Rev. E, 73(6), 2006.
 
15
F. Wu and B. A. Huberman. Novelty and collective attention. PNAS, 104(45):17599--17601, 2007.
16

Collaborative Colleagues:
Lars Backstrom: colleagues
Jon Kleinberg: colleagues
Ravi Kumar: colleagues