arXiv:2605.20552v1 Announce Type: cross Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph. This framework is suitable for solving online learning problems that involve graphs, such as content-based recommendation. In this problem, each recommended item is a node and its expected rating is similar to its neighbors. The goal is to recommend items that have high expected ratings. We aim for the algorithms where the cumulative regret would not scale poorly wi
The continuous advancements in AI research, particularly in optimizing machine learning algorithms for complex data structures like graphs, are driving new solutions for online learning and recommendation systems.
Improved recommender systems translate directly into more efficient content delivery, e-commerce, and personalized experiences, enhancing user engagement and economic value across digital platforms.
This research could lead to more sophisticated and personalized AI agents capable of understanding nuanced user preferences and contextual data on graphs, moving beyond traditional recommendation models.
- · AI researchers
- · E-commerce platforms
- · Content streaming services
- · Social media companies
- · Generic recommendation algorithms
- · Companies with static user profiling
More accurate and efficient content and product recommendations for end-users.
Increased user engagement and monetization opportunities for platforms leveraging advanced recommendation AI.
The development of highly adaptive and context-aware AI agents that can anticipate user needs across diverse digital environments.
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Read at arXiv cs.LG