Abstract
e-Advertising is the rapidly growing e-commerce application which acts a significant entity for user browsing behavior/patterns and is employed by majority of ad platforms to evaluate the ad selection process for choosing the right product. Existing search engines adopt other link structure/content-oriented and do not consider the browsing patterns. In link structure, rank scores are applied evenly irrespective of the links as target web pages are self-descriptive and use links for navigating. In a content-oriented, the web page lacks in having the rich content description to match with the search query. Thus, generating the top priority list for the user query is a difficult task. Here, mobile agent architecture is proposed to perform the search engine process by tracking the ad relevance and to estimate the probability of views/clicks to distribute the rank scores based on the ad popularity. Mobile agents extend to apply classification technique to classify the ad list into three classes and display the most relevant ads which improve the result list.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
X. Wang, W. Li, Y. Cui, R. Zhang, and J. Mao, “Click-through rate estimation for rare events in online advertising,” in Online Multimedia Advertising: Techniques and Technologies, pp. 1–12, 2011.
Cheng. H. and Cantu-Paz E. 2010: Personalized click prediction in sponsored search. In the proceedings of 3rd ACM International Conference on WEB search and Data Mining 351–360 (18).
Georgios Theocharous, Philip S. Thomas, Mohammad Ghavamzadeh: Ad Recommendation Systems for Life-Time Value Optimization WWW 2015 Companion, May 18–22, 2015, Florence, Italy. ACM 978-1-4503-3473-0/15/05.3.
Long-Sheng Chen, Tai-Cheng Kuo: Using Decision Trees to identify key Factors of Keyword Advertisements. Proceedings of the International Multi-Conference of Engineers and Computer Scientists 2014 Vol-1 IMECS 2014, March 12–14, 2014, Hong Kong.
Nirmala C R, Dr V Rama Swamy and Jyoth id M N: Mobile Agents for Audio Search and retrieval. 2010 International Journal of Computer Applications (0975–8887) Vol. 1. No. 23 (17).
Michael Bendersky, Evgeniy Gabrilovich, Vanja Josifovski and Donald Metzlre: The Anatomy of an Ad: Structures Indexing and Retrieval for Sponsored Search. WWW 2010, April 26–30 Raleigh, North Carolina, USA (16).
Zhipeng Fang, Kun Yue, Jixian Zhang, Dehai Zhang, and Weiyi Liu: Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 818203, 9 pages http://dx.doi.org/10.1155/2014/818203.
Kushal Dave, Vasudeva Verma: Computational Advertising- Techniques for Targeting Relevant Ads. Foundations and Trends informational Retrieval Vol. 8, No. 4–5 (2014) 263–418.
Bernard J. Jansen and Zhe Liu: The Effect of Ad Rank on the performance of Keyword Advertising Campaigns. Journal of the American Society for Information Science and Technology 2013.
Weinan Zhang, Dingquan Wang, Gui-Rong Xue, Hongyuan Zha: Advertising Keywords Recommendation for Short-Text Web Pages Using Wikipedia, ACM Transactions on Intelligent Systems and Technology, Vol. 3, No. 2, Article 36, Publication date: February 2012.
Jin-Yong Jung, Jung-Hyun Lee, Jong Woo Ha and Sang Keun Lee: Vocabulary Expansion Technique for Advertisement Classification, KSII Transactions on Internet and Information Systems Vol 6 No. 5 May-2012.
Andrei Broder, Marcus Fontoura, Vanja Josifovski and Lance Riedel: A semantic Approach to Contextual Advertising SIGIR’07 Copyright 2007 ACM. (23).
Cristo M., Ribeiro Neto B., Golgher P B., and De Moura E. 2006: Search Advertising in the proceedings of the StudFuzz Conference 197, 259–285 (26).
Chuan-Feng Chiu, Timothy K. Chi and Ying-Hong Wang “An Integrated Analysis, Strategy and Mobile Agent Framework for Recommendation System in EC Over Internet” Tamkang Journal of Science and Engineering, Vol. 5 No. 3, pp 159–174 (2002).
Bhanu c, Vattikonda, Santhosh Kodipaka, Hongya Zhou, Vacha Dave, Saikat Guha and Alex C Snoeren: Interpreting Advertiser Intent in Sponsored Search Publication rights licensed to ACM 978-1-4503-3664-2/15/98.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Roopa, G.M., Nirmala, C.R. (2019). Intelligent Mobile Agent Framework for Searching Popular e-Advertisements. In: Panigrahi, C., Pujari, A., Misra, S., Pati, B., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-13-0224-4_17
Download citation
DOI: https://doi.org/10.1007/978-981-13-0224-4_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0223-7
Online ISBN: 978-981-13-0224-4
eBook Packages: EngineeringEngineering (R0)