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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title How Good are LLMs in Generating Personalized Advertisements?
Organization Unit
  • Elyas Meguellati
  • Lei Han
  • Abraham Bernstein
  • Shazia Sadiq
  • Gianluca Demartini
Presentation Type paper
Item Subtype Original Work
Refereed No
Status Published in final form
  • English
ISBN 979-8-4007-0172-6
Page Range 826 - 829
Event Title WWW '24: The ACM Web Conference 2024
Event Type conference
Event Location Singapore, Singapore
Event Start Date May 13 - 2024
Event End Date May 17 - 2024
Series Name Proceedings of the International World Wide Web Conference
Publisher ACM Digital library
Abstract Text In this paper, we explore the potential of large language models (LLMs) in generating personalized online advertisements (ads) tailored to specific personality traits, focusing on openness and neuroticism. We conducted a user study involving two tasks to understand the performance of LLM-generated ads compared to human-written ads in different online environments. Task 1 simulates a social media environment where users encounter ads while scrolling through their feed. Task 2 mimics a shopping website environment where users are presented with multiple sponsored products side-by-side. Our results indicate that LLM-generated ads targeting the openness trait positively impact user engagement and preferences, with performance comparable to human-written ads. Furthermore, in both scenarios, the overall effectiveness of LLM-generated ads was found to be similar to that of human-written ads, highlighting the potential of LLM-generated personalised content to rival traditional advertising methods with the added advantage of scalability. This study underscores the need for cautious consideration in the deployment of LLM-generated content at scale. While our findings confirm the scalability and potential effectiveness of LLM-generated content, there is an equally pressing concern about the ease with which it can be misused.
Free access at DOI
Digital Object Identifier 10.1145/3589335.3651520
PDF File Download from ZORA
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Keywords Large language models, Personalization, Bias, User Engagement