Short Video Recommendation through Multimodal Feature Fusion with Attention Mechanism
Bibliographic record
Abstract
<title>Abstract</title> To address the issue of information overload, recommendation systems have emerged. These systems analyze user attributes and historical behavior to gain profound insights into user needs, thereby filtering and selecting content that aligns with those needs. Challenges in Applying Traditional Recommendation Methods to Video Recommendation: Addressing issues such as cold start problems, sparsity, and over-specialization when traditional recommendation methods are applied to video recommendation. Different Recommendation Modes: This includes sorting-based video recommendations, recommendations based on video summaries, and multi-modal or cross-modal recommendations. This paper primarily focuses on attention-based feature representation models at the feature level, learning features from different perspectives. Simultaneously, addressing the specific scenario of short video recommendations, where current approaches often rely on user preferences, user behavior, and other metadata for recommendations, neglecting the content of the short videos themselves, leading to a severe cold start problem and resulting in the neglect of most short videos. Hence, building upon efficient feature representation, this paper considers short video content and social relationships to explore user preferences from multimodal video content and social connections, enhancing the model's predictive accuracy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".