追踪音乐流媒体平台上的可供性与条目采纳
Tracing Affordance and Item Adoption on Music Streaming Platforms
AI总结：
本文基于 Deezer 用户两年数据，考察自然、算法与编辑三类平台可供性及推荐采纳行为，揭示用户实践高度异质、不存在统一采纳模式。
摘要：
流行的音乐流媒体平台通过三类可供性——自然、算法和编辑访问模式——为用户提供多样化的内容探索网络。这些平台发展在带来巨大发现潜力的同时，也使现代用户每天面临两方面的采纳决策：平台可供性采纳，以及对其中推荐内容的采纳。在为期2年的观察期内，我们追踪了一组经过仔细约束的 Deezer 用户，通过依据用户的每日时间使用情况、对主要平台可供性的采纳，以及他们对这些可供性的反应方式（尤其是推荐采纳方面）来区分用户，从而探索广义上驱动用户行为的因素。与推荐效果研究中常见的视角不同，我们假设并证实，用户在使用和采纳平台可供性方面表现出非常多样化的行为。由此形成的复杂且相当异质的图景表明，对于推荐功能和推荐内容的采纳实践，并不存在统一答案。
英文摘要：
Popular music streaming platforms offer users a diverse network of content exploration through a triad of affordances: organic, algorithmic and editorial access modes. Whilst offering great potential for discovery, such platform developments also pose the modern user with daily adoption decisions on two fronts: platform affordance adoption and the adoption of recommendations therein. Following a carefully constrained set of Deezer users over a 2-year observation period, our work explores factors driving user behaviour in the broad sense, by differentiating users on the basis of their temporal daily usage, adoption of the main platform affordances, and the ways in which they react to them, especially in terms of recommendation adoption. Diverging from a perspective common in studies on the effects of recommendation, we assume and confirm that users exhibit very diverse behaviours in using and adopting the platform affordances. The resulting complex and quite heterogeneous picture demonstrates that there is no blanket answer for adoption practices of both recommendation features and recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
