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Predictive Modeling of Short-Form Video Interaction : Leveraging LMM LLaVA for Enhanced Accuracy

숏폼 영상 상호작용 예측 모델링 : LMM LLaVA 모델을 활용한 정확도 향상 접근법

목차

1. Introduction 7
1.1 Research Background 8
1.2 Research Purpose 11
2. Literature Review and Theoretical Background 14
2.1. Short-Form Videos and User Behavior 15
2.2. Analysis and Prediction Models Using XGBoost in Marketing 17
2.3. Analysis Using LMM in Marketing 19
3. Research Methodology 21
3.1. Data Collection 22
3.1.1. TikTok Meta Data Processing: Log Normalization 24
3.1.2. TikTok Text Data Processing: Embedding and PCA 26
3.1.3. Use of LLaVA to Generate Video Description Text 28
3.2. Machine Learning Model Development 32
3.2.1. XGBoost Model Structure 33
3.2.2. XGBoost hyperparameter Tuning 35
4. Analysis and Results 38
4.1. SHAP 40
4.2. XGBoost Prediction Model: Scenario-Specific Analysis 44
4.2.1. Scenario 1: Likes 46
4.2.2. Scenario 2: Saves 47
4.2.3. Scenario 3: Comments 47
4.2.4. Scenario 4. Total Interaction 48
4.3. t-SNE 51
5. General Discussion 57
5.1. Implications 59
5.2. Limitations and Future Research 62
6. Reference 65

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