A Framework to Harvest Page Views of Web for Banner Advertising
Pages 57 - 68
Abstract
Online advertising provides an opportunity for product sellers and service providers to reach customers and has become a key factor in the growth of economy. It is a major source of revenue for the major search engine and social networking sites. Search engine, context-specific and banner advertising are the major modes of online advertising. The banner advertisement mode has certain advantages over other modes of advertising. Currently, the number of websites registered comes to a billion. Each day, a typical website receives the number of visitors ranging from hundreds to millions. In a few years, the entire population of the globe is going to be connected to Internet and browse websites. It is possible for a product seller or service provider to reach every potential customer through banner advertising. In this paper, a framework is proposed to harvest the pages views of web by forming the clusters of similar websites. Rather than managing a single website, the publisher manages the aggregated advertising space of a collection of websites. As a result, the advertisement space could be expanded significantly and it will provide the opportunity for increased number of publishers to market the aggregated advertisement space of millions of websites to advertisers for reaching potential customers. It will also help in balancing the management of banner advertising market.
References
[1]
Interactive Advertising Bureau. http://www.iab.net
[2]
Internet Live Stats 2015. http://www.internetlivestats.com/total-number-of-websites
[3]
Online advertising 2015. https://en.wikipedia.org/wiki/Online advertising
[4]
Right Media 2015. https://en.wikipedia.org/wiki/Right_Media
[5]
Adler, M., Gibbons, P.B., Matias, Y.: Scheduling space-sharing for internet advertising. J. Sched. 52, 103---119 2002
[6]
Amiri, A., Menon, S.: Efficient scheduling of internet banner advertisements. ACM Trans. Internet Technol. TOIT 34, 334---346 2003
[7]
Bleier, A., Eisenbeiss, M.: Personalized online advertising effectiveness: the interplay of what, when, and where. Mark. Sci. 345, 669---688 2015
[8]
Budhiraja, A., Reddy, P.K.: An approach to cover more advitisers in adwords. In: 2015 International Conference on Data Science and Advanced Analytics DSAA accepted: to be published, 2015
[9]
Caruso, F., Giuffrida, G., Zarba, C.: Heuristic Bayesian targeting of banner advertising. Optim. Eng. 161, 247---257 2015.
[10]
Claessens, J., Díaz, C., Faustinelli, R., Preneel, B.: A secure and privacy-preserving web banner system for targeted advertising. Electronic Commerce Research 2003
[11]
Contantin, R., Feldman, J., Muthukrishnan, S., Pal, M.: Online ad slotting with cancellations. In: Fourth Workshop on Ad Auctions; Symposium on Discrete Algorithms SODA 2009
[12]
Ester, M., Kriegel, H.P., Schubert, M.: Web site mining: a new way to spot competitors, customers and suppliers in the world wide web. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 249---258. ACM 2002
[13]
Feige, U., Immorlica, N., Mirrokni, V., Nazerzadeh, H.: A combinatorial allocation mechanism with penalties for banner advertising. In: Proceedings of the 17th International Conference on World Wide Web, pp. 169---178. ACM 2008
[14]
Gallagher, K., Parsons, J.: A framework for targeting banner advertising on the internet. In: 1997 Proceedings of the Thirtieth Hawaii International Conference on System Sciences, vol. 4, pp. 265---274. IEEE 1997
[15]
Ghosh, A., McAfee, P., Papineni, K., Vassilvitskii, S.: Bidding for representative allocations for display advertising. In: Leonardi, S. ed. WINE 2009. LNCS, vol. 5929, pp. 208---219. Springer, Heidelberg 2009
[16]
Kazienko, P., Adamski, M.: Personalized web advertising method. In: De Bra, P.M.E., Nejdl, W. eds. AH 2004. LNCS, vol. 3137, pp. 146---155. Springer, Heidelberg 2004
[17]
Kazienko, P., Adamski, M.: Adrosa---adaptive personalization of web advertising. Inf. Sci. 17711, 2269---2295 2007
[18]
Kenekayoro, P., Buckley, K., Thelwall, M.: Clustering research group website homepages. Scientometrics 1023, 2023---2039 2015
[19]
Kumar, S., Dawande, M., Mookerjee, V.S.: Optimal scheduling and placement of internet banner advertisements. IEEE Transactions on Knowledge and Data Engineering 1911, 1571---1584 2007
[20]
Kumar, S., Jacob, V.S., Sriskandarajah, C.: Scheduling advertisements on a web page to maximize revenue. Eur. J. Oper. Res. 1733, 1067---1089 2006
[21]
Nakamura, A., Abe, N.: Improvements to the linear programming based scheduling of web advertisements. Electron. Commer. Res. 51, 75---98 2005
[22]
Nazerzadeh, H., Saberi, A., Vohra, R.: Dynamic cost-per-action mechanisms and applications to online advertising. In: Proceedings of the 17th International Conference on World Wide Web, pp. 179---188. ACM 2008
[23]
Rogers, A., David, E., Payne, T.R., Jennings, N.R.: An advanced bidding agent for advertisement selection on public displays. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2007, pp. 51:1---51:8. ACM, New York 2007. http://doi.acm.org/10.1145/1329125.1329186
[24]
Srinivas, P.G., Reddy, P.K., Bhargav, S., Kiran, R.U., Kumar, D.S.: Discovering coverage patterns for banner advertisement placement. In: Chawla, S., Ho, C.K., Bailey, J., Tan, P.-N. eds. PAKDD 2012, Part II. LNCS, vol. 7302, pp. 133---144. Springer, Heidelberg 2012
[25]
Srinivas, P.G., Reddy, P.K., Trinath, A.V.: CPPG: efficient mining of coverage patterns using projected pattern growth technique. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. eds. PAKDD 2013 Workshops. LNCS, vol. 7867, pp. 319---329. Springer, Heidelberg 2013
[26]
Srinivas, P.G., Reddy, P.K., Trinath, A., Bhargav, S., Kiran, R.U.: Mining coverage patterns from transactional databases. J. Intell. Inf. Syst., 1---17 2014
[27]
Su, Q., Chen, L.: A method for discovering clusters of e-commerce interest patterns using click-stream data. Electron. Commer. Res. Appl. 141, 1---13 2015
[28]
Trainor, D., Choc, T.N., Ainslie, A.N.: Automatically grouping resources accessed by a user. US Patent 9,043,464, 26 May 2015
[29]
Trinath, A., Gowtham Srinivas, P., Krishna Reddy, P.: Content specific coverage patterns for banner advertisement placement. In: 2014 International Conference on Data Science and Advanced Analytics DSAA, pp. 263---269. IEEE 2014
[30]
Wu, C.: Matching markets in online advertising networks: The tao of taobao and the sense of adsense 2012. Accessed 18 October 2012
Recommendations
Coverage patterns for efficient banner advertisement placement
WWW '11: Proceedings of the 20th international conference companion on World wide webIn an online banner advertising scenario, an advertiser expects that the banner advertisement should be displayed to certain percentage of web site visitors. In this context, to generate more revenue for a given web site, the publisher has to meet the ...
An Approach to Allocate Advertisement Slots for Banner Advertising
CODS '16: Proceedings of the 3rd IKDD Conference on Data Science, 2016In the banner advertising scenario, an advertiser aims to reach the maximum number of potential visitors and a publisher tries to meet the requests of increased number of advertisers to maximize the revenue. In the literature, a model was introduced to ...
Morphing Banner Advertising
Researchers and practitioners devote substantial effort to targeting banner advertisements to consumers, but they focus less effort on how to communicate with consumers once targeted. Morphing enables a website to learn, automatically and near optimally,...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 15 December 2015
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 10 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in