An adaptive system can be excellent at updating parameters and still be poor at adaptation.
That is not a paradox. It is a sequencing error.
When adaptation logic reads only user state and ignores what is being adapted, the system optimizes the wrong level of the problem. It can become responsive in a technical sense while becoming incoherent in an experiential sense.
The missing layer is perceptual grounding.
Before asking “how should this change for this person,” a system has to answer “what is this content likely to mean as perceived.” Without that, personalization is forced to operate over a weak representation. It cannot reliably distinguish between changes that are technically valid and changes that are experientially misplaced.
This is why personalization should be treated as a second-order capability:
- Perception first. Model content semantics and perceptual affordances well enough to constrain adaptive moves.
- Coherence second. Validate that adaptation steps preserve scene logic, tone, and interpretive fit.
- Personalization third. Learn user-specific policy over a representation that is already perceptually sane.
The point is not to demote personalization. The point is to prevent it from being asked to do impossible work.
Optimization can refine a policy; it cannot supply missing perception.
Related
Sources
- Gediminas Adomavicius and Alexander Tuzhilin. “Context-Aware Recommender Systems.” In Recommender Systems Handbook, 2011. DOI: 10.1007/978-0-387-85820-3_7.
- Li Chen and Pearl Pu. “Critiquing-Based Recommenders: Survey and Emerging Trends.” User Modeling and User-Adapted Interaction 22:125-150, 2012. DOI: 10.1007/s11257-011-9108-6.