User Clustering Algorithms in Online Advertising
Honey Vachharajani1, Rajeev Gupta2, Nikhlesh Pathik3
1Honey Vachharajani, Department of Computer Engineering, R.G.P.V, S.I.S.T.E.C Gandhinagar, Bhopal (M.P), India.
2Rajeev Gupta, Department of Computer Engineering, S.I.S.T.E.C Gandhinagar, Bhopal (M.P), India.
3Nikhlesh Pathik, Department of Computer Engineering, S.I.S.T.E.C Gandhinagar, Bhopal (M.P), India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 27 August 2019 | PP: 29-35 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10060782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1006.0782S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Digital Advertising has emerged as one of the main source of revenue for major part of Internet economy. The audience communicates with the digital world by using search engines, social networking, online market- ing and banking sites and many more. To generate more income through advertisement the ad-publishers and the ad-networks need to be watchful about users interest when targeting them for their brand or product pro- motion through these channels. The placement of advertisement depends on the users interest is as it involves the higher probability of a click on the ad, which offers benefit to all the entities, involved. This paper surveys different clustering approaches proposed by various authors for user clustering. At the end of the paper, the various methods are compared based on phases, techniques and usages.
Keywords: Audience Clustering, Unsupervised Learning, User Profiling, Ad Targeting Clustering Algorithms.
Scope of the Article: Clustering