Personalityzation: UI Personalization, Theoretical Grounding in HCI and Design Research
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".