A Enhancing Video Recommendation of Multi-Headed Self-Attentive with Multimodal Features
Bibliographic record
Abstract
Abstract The exponential growth of the internet has led to the creation of a large volume of information, making it challenging for users to navigate through. As a solution to this challenge, recommendation systems have emerged. In video recommendation, besides applying some basic interaction data (including image data, behavior data, context data, etc.) to the recommendation model, many studies also try to apply the video content data to the model for video recommendation. In this paper, we propose different types of features for different modal data, and select the most suitable feature types according to different tasks. The internal representation of features is learned by a multi-headed self-attentive mechanism and the cross-representation of features is learned by attention. On the basis of this, the multimodal features of video data are represented and modeled in a unified way, which is the basis for the implementation of multi-view video recommendation. and based on this, we add multimodal video content to mine richer and more comprehensive descriptions, so as to provide more accurate and personalized recommendations. The results of experiments conducted on real data sets demonstrate the effectiveness of the model proposed in this paper.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".