Shall IoT User Interfaces Start Recommending Multimedia Devices as Well?
Bibliographic record
Abstract
Recommendation of media objects, such as audio and video clips, has been there for a while. Most user interfaces include a list of favourites or most popular media objects. On the other hand, recommendation of multimedia devices is limited to shopping websites. With the evolution of IoT, however, users these days are surrounded by many interconnected devices that can be used to accomplish the same task at any given time. For example, while at home, a user can choose to play a media le on a smartphone, tablet, laptop, TV, or home theatre. In this article we investigate the question of whether or not users are ready to accept automatic recommendation of physical things with a case study of media playback devices. We further investigate various factors that a ect user's choice of media playback device with a user study. The analysis shows that users like device recommendation in general. In addition, many users even prefer the playback to be automatically transferred directly to the most appropriate device, while other users just want a noti cation. We also found that user's gender, profession, age, and the duration of media le a ect the choice of playback device.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".