ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation
arXiv:2605.28293v1 Announce Type: new Abstract: Proactive Recommender Systems (PRSs) aim to guide user preference shift toward target items by generating paths of intermediate recommendations. Reinforcement learning (RL) provides a principled framework for optimizing such sequential decision tasks, as path rewards can naturally capture both short-term acceptance and long-term guidance effectiveness. However, naively applying policy gradients to PRS results in deficient gradient estimation. We identify two deficiencies: (1) path-level rewards decompose into step-level rewards with positive mean
The continuous improvement in AI research, particularly in reinforcement learning and recommender systems, drives the exploration of more sophisticated and 'proactive' methods for user interaction.
This research represents advancements in AI's ability to not just react but to proactively shape user behavior, with significant implications for commerce, content consumption, and personalized experiences.
The effectiveness of reinforcement learning in recommendation systems could see a leap, leading to more intelligent and influential AI-driven platforms that guide user choices subtly.
- · E-commerce platforms
- · Content streaming services
- · AI researchers and developers
- · Personalized experience providers
- · Companies relying on static recommendation algorithms
- · Users who prefer purely discovery-driven interfaces
Improved proactive recommendation systems will lead to higher user engagement and conversion rates for platforms implementing them.
The ethical implications of AI proactively guiding user preferences will become a more prominent discussion.
Enhanced 'preference shaping' capabilities could concentrate market power among platforms with superior AI recommendation technology.
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Read at arXiv cs.LG