[1]QIU Jianping.Analysis of attention model based on Internet[J].CAAI Transactions on Intelligent Systems,2013,8(4):339-343.[doi:10.3969/j.issn.1673-4785.201304039]
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Analysis of attention model based on Internet

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 4 Page number: 339-343 Column: 学术论文—机器学习 Public date: 2013-08-25
Title:
Analysis of attention model based on Internet
Author(s):
QIU Jianping
Institute of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
Keywords:
Internet;  fictitious economy;  attention model;  long-tailed distributions
CLC:
TP393.4
DOI:
10.3969/j.issn.1673-4785.201304039
Abstract:
With rapid development of Internet-based social media and their applications, Internet users’ attention has more and more important significance to a fictitious economy. In this paper, users are regarded as content conveyed in Internet while information resources are regarded as targets. Along this line, Internet is regarded as a net that distributes people’s attention stream among information resources. By collecting and analyzing the behavior data of Internet users, this paper demonstrates a flow network of transporting attention and gives a dynamic model describing Internet users’ attention. The experimental results show that, Web2.0 attracts more attention than the Web1.0 Website. The results of this paper also demonstrate that growth of attention to the website is slower than growth of traffic, and thereby creating a "diseconomies of scale" phenomenon. We were able to demonstrate that ad network and vertical niche attract more attention than search engine and affiliate network.
References:
［1］苏萌，柏林森，周涛. 个性化：商业的未来［M］. 北京：机械工业出版社， 2012： 1-20.
［2］PAN W, AHARONYM N, PENTLAND A S. Composite social network for predicting mobile apps installation［C］//Proceedings of the 25th AAAI Conference on Artificial Intelligence. Cambridge， USA， 2011: 821-827.
［3］ZHANG C J, ZENG A. Behavior patterns of online users and the effect on information filtering［J］. Physica A, 2012, 391: 1822-1830.
［4］GUO S, WANG M, LESKOVEC J. The role of social networks in online shopping: information passing, price of trust, and consumer choice［C］//Proceedings of the 12th ACM Conference on Electronic Commerce. New York, USA, 2011: 157-166.
［5］HUBERMAN A, PIROLLI P L, PITKOW J E, et al. Strong regularities in world wide web surfing［J］. Science, 1998, 280(5360): 95-96.
［6］DENNIS M.WILKINSON. Strong regularities in online peer production［C］//Proceedings of the 9th ACM Conference on Electronic Commerce. Chicago, USA, 2008: 302-309.
［7］WATTS D. Network, dynamics, and the small-world phenomenon［J］. Sociol, 1999, 105: 2063-2064.
［8］STROGATZ S. The emerging science of spontaneous order［M］. New York, USA： Hyperion press, 2003: 312-319.
［9］BARABASI A L. Network science［J］. Philosophical Transactions of the Royal Society A， 2013, 371: 1471-2962.
［10］孟凡新. 互联网时代的眼球经济：中国网民注意力聚焦何处？［EB/OL］. ［2012-10-25］. http://www.cnnic.cn/research/fxszl/fxswz/201207/t20120719_32346.html.
［11］ROBERTS J, HANN I H, SLAUGHTER S. Understanding the motivations, participation and performance of open source software developers: a longitudinal study of the apache projects［J］. Management Science, 2006, 52(7): 984-999.
［12］Google. The 1000 most-visited sites on the web［EB/OL］. ［2012-10-25］. http://www.google.com/adplanner/static/top1000.
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