Social media feeds use predictive models to maximize engagement, often misaligning how people consume content with how they wish to. We introduce Bonsai, a system that enables people to build personalized and intentional feeds. Bonsai implements a platform-agnostic framework comprising Planning, Sourcing, Curating, and Ranking modules. This framework allows users to express their intent in natural language and exert fine-grained control over a procedurally transparent feed creation process. We evaluated the system with 15 Bluesky users in a two-phase, multi-week study. We find that participants successfully used our system to discover new content, filter out irrelevant or toxic posts, and disentangle engagement from intent, but curating intentional feeds required more effort than they are used to. Simultaneously, users sought system transparency mechanisms to effectively use (and trust) intentional, personalized feeds. Overall, our work highlights intentional feedbuilding as a viable path beyond engagement-based optimization.

翻译：社交媒体信息流通常使用预测模型来最大化用户参与度，这常常导致用户实际消费内容的方式与其期望方式产生偏差。本文介绍Bonsai系统，该系统使用户能够构建个性化且基于意图的信息流。Bonsai实现了一个与平台无关的框架，包含规划、来源获取、策展和排序四大模块。该框架允许用户通过自然语言表达意图，并在过程透明的信息流创建过程中实施细粒度控制。我们通过为期数周的两阶段研究，对15名Bluesky用户进行了系统评估。研究发现：参与者成功利用本系统发现了新内容，过滤了无关或有害帖子，并将参与度与意图进行了分离；但构建基于意图的信息流所需精力超出其惯常投入。同时，用户需要系统透明度机制来有效使用（并信任）这种基于意图的个性化信息流。总体而言，我们的工作表明基于意图的信息流构建是超越参与度优化范式的可行路径。