SmartAd: A smart system for effective advertising in online videos
Bibliographic record
Abstract
Advertising in online videos is a large and growing market. In this paper, we propose a new approach to match ads with online videos based on the shopping interests of the target audience of videos. The proposed approach increases the relevance of ads to the actual viewers (humans) of videos, which increases the number of users who purchase goods and services offered by the advertisers. This in turn will increase the revenues for advertisers as well as for the video sites as video sites usually charge advertisers based on the number of user clicks on their ads. The proposed approach is different from current approaches used in practice or proposed in the literatures, which most of them try to maximize the relevance of ads to the tags or contents of videos (objects). We conduct a subjective study to evaluate the performance of the proposed approach on many videos retrieved from YouTube. Our results show that the proposed approach yields more relevant ads to viewers than the YouTube's approach. We also compare against other approaches proposed in the literature and we show that the new approach outperforms them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".