Prediction of instantaneous likeability of advertisements using deep learning
Bibliographic record
Abstract
Abstract The degree to which advertisements are successful is of prime concern for vendors in highly competitive global markets. Given the astounding growth of multimedia content on the internet, online marketing has become another form of advertising. Researchers consider advertisement likeability a major predictor of effective market penetration. An algorithm is presented to predict how much an advertisement clip will be liked with the aid of an end‐to‐end audiovisual feature extraction process using cognitive computing technology. Specifically, the usefulness of different spatial and time‐domain deep‐learning architectures such as convolutional neural and long short‐term memory networks is investigated to predict the frame‐by‐frame instantaneous and root mean square likeability of advertisement clips. A data set named the ‘BUET Advertisement Likeness Data Set’, containing annotations of frame‐wise likeability scores for various categories of advertisements, is also introduced. Experiments with the developed database show that the proposed algorithm performs better than existing methods in terms of commonly used performance indices at the expense of slightly increased computational complexity.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".