arXiv:2611.07851v1 Announce Type: new Abstract: Most recommender systems treat user preferences as a single signal to infer and optimize, yet human preferences are a mix of a stable preference and situational preferences that turn on and off depending on context. Through Tasteply, we externalize this preference structure as manipulable spatial objects. Tasteply represents each situational preference as an attractor and the stable preference as an underlying global field. Users can adjust their feed by moving around the space and adjusting the strength of the global field, turning the preference structure that remains latent within the model into something they can see and act on directly to steer the recommendation algorithm. Through an exploratory study (N=5), we found that this spatial control was effective, along with its limits and future direction as a means of manipulating preference-based recommendations.