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Rethinking Offline Personalized Advertising: Challenges and System Design

by Ashutosh Sathe, Sunil B. Mane
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 19
Year of Publication: 2021
Authors: Ashutosh Sathe, Sunil B. Mane
10.5120/ijca2021921074

Ashutosh Sathe, Sunil B. Mane . Rethinking Offline Personalized Advertising: Challenges and System Design. International Journal of Computer Applications. 174, 19 ( Feb 2021), 1-6. DOI=10.5120/ijca2021921074

@article{ 10.5120/ijca2021921074,
author = { Ashutosh Sathe, Sunil B. Mane },
title = { Rethinking Offline Personalized Advertising: Challenges and System Design },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2021 },
volume = { 174 },
number = { 19 },
month = { Feb },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number19/31782-2021921074/ },
doi = { 10.5120/ijca2021921074 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:22:31.471010+05:30
%A Ashutosh Sathe
%A Sunil B. Mane
%T Rethinking Offline Personalized Advertising: Challenges and System Design
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 19
%P 1-6
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Online personalized advertisements on a smartphone have shown a great impact on both user experience and advertiser income. This type of personalized advertising is possible due to existence of easily traceable features when user is online. Online advertising agencies such as Google AdSense can determine appropriate ads for a particular user based on their behavior on the internet. Therefore, these advertising agencies inherently depend long interactions between user and the device to get a decent ad recommendation. This paper focuses on interactions of users with electronic devices which are very short and need-based. Examples of these devices would be gaming arenas, selfie stations in malls or self check-in booths at the airport. The paper throughout considers these types of interactions as ”offline” since there is no way to track user’s behavior here like it is possible in ”online” scenario. Main objective of the paper is to discuss challenges in recommending ads in offline interaction scenario and develop methods to overcome these challenges. Finally, the paper presents a method to recommend ads using fashion based features with the help of computer vision and demonstrates its working.

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Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning Power Efficient Machine Learning Computer Vision Personalized Advertising Computer Vision in Embedded Systems

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