Context-aware interactive content adaptation
Bibliographic record
Abstract
Automatic adaptation of content for mobile devices is a challenging problem because optimal adaptation often depends on the usage semantics of content, as well as the context of users (e.g., screen size of device being used, network connectivity, location, etc.). UsageawaRe Interactive Content Adaptation (URICA) is an automatic technique that adapts content for mobile devices based on usage semantics. URICA allows a user who is unsatisfied with the system’s current adaptation prediction to take control of the adaptation process and make changes until the content is suitably adapted for her purposes. The adaptation system learns from the user’s modifications and adjusts its prediction for future accesses by other users. This paper shows that it is possible to exploit user interaction to learn how to adapt content based on context. We introduce Feedback-driven Context Selection (FCS), an automatic technique that leverages user interaction to identify the context that has the most impact on adaptation requirements. We added contextawareness to URICA so that it makes adaptation predictions for a user based only on the history of the community of users that share the context identified by FCS. The result is an automatic adaptation system that provides fine grain adaptations that reflect both the user’s context and the content’s usage semantics. This level of fine grain adaptation was previously available only in content that was customized manually. Experiments with two context-aware URICA prototypes show that FCS correctly identifies the contextual characteristics that impact adaptation requirements, and that grouping users into communities based on context improves the performance of the adaptation system by up to 79%.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".