arXiv:2501.07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a bandit problem with delayed rewards. There is an apparent trade-off in choosing the learning signal: waiting for the full reward to become available might take several weeks, slowing the rate of learning, whereas using short-term proxy rewards reflects the actual long-term goal only imperfectly. First, we develop a predictive model of delayed rewards that incorporates al
The increasing sophistication of recommender systems and the demand for long-term user satisfaction are driving innovation in reinforcement learning with delayed rewards.
Optimizing for long-term satisfaction in systems like content recommendations has significant implications for user engagement, platform stickiness, and ultimately, economic value in the digital economy.
This research outlines a method to better handle the trade-off between immediate learning signals and truly optimizing for delayed, long-term outcomes, potentially making AI systems more effective at fostering sustained engagement.
- · Tech platforms with recommender systems
- · Advertisers and content creators
- · Users of AI-powered services
- · Machine learning researchers
- · Platforms with naive short-term optimization
- · Content providers relying on clickbait
Recommender systems become more adept at understanding and predicting long-term user preferences.
Increased user retention and satisfaction across various digital services and platforms.
Deeper, more meaningful engagement with digital content and services, potentially reshaping consumption patterns and attention economies.
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Read at arXiv cs.AI