Let AI Entertain You: Increasing User Engagement with Generative AI and Rejection Sampling
Jingying Zeng, Jaewon Yang, Waleed Malik, Xiao Yan, Richard Huang, Qi, He

TL;DR
This paper introduces a framework using rejection sampling to improve user engagement with generative AI by incorporating user feedback, demonstrated through email notification subject lines that increased engagement metrics.
Contribution
It presents a novel, universal framework leveraging rejection sampling and user feedback to enhance engagement in generative AI applications, especially when standard methods fall short.
Findings
Achieved +1% session lift in email engagement
Realized +0.4% increase in weekly active users
Demonstrated effectiveness of feedback-driven rejection sampling
Abstract
While generative AI excels in content generation, it does not always increase user engagement. This can be attributed to two main factors. First, generative AI generates content without incorporating explicit or implicit feedback about user interactions. Even if the generated content seems to be more informative or well-written, it does not necessarily lead to an increase in user activities, such as clicks. Second, there is a concern with the quality of the content generative AI produces, which often lacks the distinctiveness and authenticity that human-created content possesses. These two factors can lead to content that fails to meet specific needs and preferences of users, ultimately reducing its potential to be engaging. This paper presents a generic framework of how to improve user engagement with generative AI by leveraging user feedback. Our solutions employ rejection sampling,…
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Taxonomy
TopicsPersonal Information Management and User Behavior · Recommender Systems and Techniques · FinTech, Crowdfunding, Digital Finance
