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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 | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | No |
| Status | Published in final form |
| Language |
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| 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 |
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| Keywords | Large language models, Personalization, Bias, User Engagement |