Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems
arXiv:2411.13865v4 Announce Type: replace-cross Abstract: Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. The central challenge is to balance content exploration and exploitation while allowing users to adjust their recommendation preferences. Ideally, this balance can be captured with a hierarchical representation, where depth search facilitates exploitation and breadth search enables exploration. However, existing approaches face two fundamental limitations: Euclidean methods struggle to capture hierarchical structures, while hyper
The proliferation of AI-driven content platforms has made the 'information cocoon' effect a critical and widely recognized problem, demanding innovative solutions beyond current limitations.
This research addresses a core challenge in AI's application – ensuring user discovery and mitigating filter bubbles, which impacts user experience, market dynamics, and information diversity.
The proposed hyperbolic framework offers a new mathematical approach to recommender systems, potentially leading to more balanced and adaptable content discovery engines.
- · AI platform developers
- · Content creators
- · Consumers seeking diverse content
- · Recommendation engine researchers
- · Platforms relying heavily on exploitation-only algorithms
- · Models limited by Euclidean-based approaches
Recommender systems become more adept at balancing new content discovery with user preferences, reducing echo chambers.
Improved recommendation diversity could lead to shifts in content consumption patterns and potentially new market opportunities for niche content.
Enhanced control over recommendation preferences might increase user engagement and trust in AI systems, influencing regulatory approaches to content moderation and algorithmic transparency.
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Read at arXiv cs.LG