A Learning Approach to Interactive Advertising

Tu, Peter; Hamilton, D. W. Wilson; Gao, Tao; Chen, Alex; Foehner, Justin; Pang, Alberto Santamaria; Kubricht, James

doi:10.1109/avss.2019.8909885

Cited by 2 publications

(1 citation statement)

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“…Interactions include playing trivia games and bingo. An interactive advertising system, described by Tu et al (2019), is comprised of a flat screen display, a collocated sensing camera, and an outcome feedback camera. The display presents an Avatar that has the goal of convincing people to enter a store.…”

Section: Physical Agencymentioning

confidence: 99%

Artificial Intelligence Community and Self

2021

AI Magazine

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The means by which artificial intelligence systems will both interact and influence individuals and community are considered, with a description of current capabilities such as video analytics, agency, natural language processing, and online data analysis. Open research questions such as the roles of analogies, associative memory, and the grounding problem are discussed, with speculation presented regarding the possible ramification of this research agenda. Such topics will include how artificial intelligence and humanity might co‐evolve; artificial creativity; and life without work.

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Section: Physical Agencymentioning

confidence: 99%

Artificial Intelligence Community and Self

2021

AI Magazine

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Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework

Viktoratos

Tsadiras

2021

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This work conducts a systematic literature review about the domain of personalized advertisement, and more specifically, about the techniques that are used for this purpose. State-of-the-art publications and techniques are presented in detail, and the relationship of this domain with other related domains such as artificial intelligence (AI), semantic web, etc., is investigated. Important issues such as (a) business data utilization in personalized advertisement models, (b) the cold start problem in the domain, (c) advertisement visualization issues, (d) psychological factors in the personalization models, (e) the lack of rich datasets, and (f) user privacy are highlighted and are pinpointed to help and inspire researchers for future work. Finally, a design framework for personalized advertisement systems has been designed based on these findings.

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A Learning Approach to Interactive Advertising

Cited by 2 publications

References 10 publications

Artificial Intelligence Community and Self

Artificial Intelligence Community and Self

Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework

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