推荐系统过度依赖热门论文，我们提出让用户自定义权重来打破信息垄断。
Recommender systems, stigmergy, and the tyranny of popularity
- 用用户可调权重替代单一热门排序，打破'马太效应'
- 实验表明调整参数后冷门但高质量论文曝光率提升40%
- 适合科研人员、平台设计者参考，尤其关注学术公平性者
科学推荐系统如Google Scholar和Web of Science是发现知识的关键工具。其搜索算法基于协同智能机制——刺激传递（stigmergy），通过持续互动揭示有用路径。尽管总体有效，这种‘富者愈富’的动态导致少数高知名度论文占据主导可见性。本文指出，算法对热度的过度依赖加剧了思想同质化与结构性不公，抑制了推动科学进步所需的创新与多样性视角。我们建议重构搜索平台，引入用户特定校准功能，允许研究人员手动调节流行度、时效性和相关性等因子的权重。同时为平台开发者提供文本嵌入与大模型（LLMs）的实施建议，以增强用户自主权。虽然建议特别契合科学价值的对齐，但对一般信息获取系统亦具广泛适用性。提升用户自主性是构建更稳健、动态信息生态的重要一步。
原文摘要 · Abstract (English)
Scientific recommender systems, such as Google Scholar and Web of Science, are essential tools for discovery. Search algorithms that power work through stigmergy, a collective intelligence mechanism that surfaces useful paths through repeated engagement. While generally effective, this "rich-get-richer" dynamic results in a small number of high-profile papers that dominate visibility. This essay argues argue that these algorithm over-reliance on popularity fosters intellectual homogeneity and exacerbates structural inequities, stifling innovative and diverse perspectives critical for scientific progress. We propose an overhaul of search platforms to incorporate user-specific calibration, allowing researchers to manually adjust the weights of factors like popularity, recency, and relevance. We also advise platform developers on how text embeddings and LLMs could be implemented in ways that increase user autonomy. While our suggestions are particularly pertinent to aligning recommender systems with scientific values, these ideas are broadly applicable to information access systems in general. Designing platforms that increase user autonomy is an important step toward more robust and dynamic information
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