Toward Faceted Skill Recommendation in Intelligent Personal Assistants
Bibliographic record
Abstract
Research continuously shows that, despite the wide range of skills developed for Intelligent Personal Assistants (IPAs), users tend to engage with only a small number of them. One reason for this discrepancy is the issue of skill discoverability, which is commonly addressed through conversational recommendations. Current recommendation strategies, however, are limited due to information asymmetry, lack of interactivity, and an underdeveloped understanding of appropriate grouping of available skills. In this paper, we explore opportunities for interactive faceted skill recommendations using voice interfaces. Through an open card sort user study and semi-structured interviews, we identify and describe five facets driving users’ natural grouping of IPA skills (Thematic, Procedural, Cross-system, Environmental, and Recipient), and demonstrate the need for simultaneous support of these facets. We then discuss the implications of these findings for advancing the discoverability of IPA skills through the design of interactive conversational recommendations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".