Bibliographic record
Abstract
Automated software customization is drawing increasing attention as a means to help users deal with the scope, complexity, potential intrusiveness, and ever-changing nature of modern software. The ability to automatically customize functionality, interfaces, and advice to specific users is made more difficult by the uncertainty about the needs of specific individuals and their preferences for interaction. Following recent probabilistic techniques in user modeling, we model our user with a dynamic Bayesian network (DBN) and propose to explicitly infer the type --- a composite of personality and affect variables --- in real time. We design the system to reason about the impact of its actions given the user's current attitudes. To illustrate the benefits of this approach, we describe a DBN model for a text-editing help task. We show, through simulations, that user types can be inferred quickly, and that a myopic policy offers considerable benefit by adapting to both different types and changing attitudes. We then develop a more realistic user model, using behavioural data from 45 users to learn model parameters and the topology of our proposed user types. With the new model, we conduct a usability experiment with 4 users and 4 different policies. These experiments, while preliminary, show encouraging results for our adaptive policy.
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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 | 0.003 | 0.022 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".