In interactive recommendation settings, optimizing primarily for estimated relevance often leads to recommendation lists that over-emphasize familiar and popular items, which can reduce discovery and undermine longer-term value. Individual-level multi-objective control can enable recommendation lists that better reflect heterogeneous user goals, by translating explicit preference signals into objective trade-offs that the recommender is designed to …

In multi-objective recommender systems, system-side metrics are often used both to optimize and to label user-facing controls, but this practice can misalign with users’ conceptual understanding of objectives, which in turn undermines tuning effectiveness, transparency, and satisfaction. Empirical measurement of perception can enable more interpretable objective controls and more defensible metric choices by explicitly linking …