Personalityzation: UI Personalization, Theoretical Grounding in HCI and Design Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Personalization is an effective means for accommodating differences between individuals. Therefore, the personalization of a system’s user interface (UI) features can enhance usability. To date, UI personalization approaches have been largely divorced from psychological theories of personality, and the user profiles constructed by extant personalization techniques do not map directly onto the fundamental personality traits examined in the psychology literature. In line with recent calls to ground the design of information systems in behavioral theory, we maintain that personalization that is informed by psychology literature is advantageous. More specifically, we advocate an approach termed “personalityzation”, where UI features are adapted to an explicit model of a user’s personality. We demonstrate the proposed personalityzation approach through a proof-of-concept in the context of social recommender systems. We identify two key contributions to information systems research. First, extending prior works on adaptive interfaces, we introduce a UI personalization framework that is grounded in psychology theory of personality. Second, we reflect on how our proposed personalityzation framework could inform the discourse in design research regarding the theoretical grounding of system’s design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it