M3TR: Temporal Retrieval Enhanced Multi-Modal Micro-video Popularity Prediction

arXiv:2411.15455 · cs.MM, cs.AI · Submitted 2026-08-18 · Read on arXiv

Jiacheng Lu, Weijian Wang, Mingyuan Xiao, Yang Hua, Tao Song, Bo Peng, Cheng Hua, Haibing Guan

cs.MM, cs.AI

Submitted: 2026-08-18

Updated: 2026-08-19

Terminology

• Embeddings — 22× in this paper · explained in 2 episodes
• Core — 20× in this paper · explained in 2 episodes
• Mamba-Hawkes Process — 17× in this paper
• Transformer — 14× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Feature Extraction — 9× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Fidelity — 7× in this paper · explained in 2 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• alpha — 7× in this paper · explained in 1 episode
• Attention Mechanism — 6× in this paper · explained in 3 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Archetype — 4× in this paper · explained in 1 episode
• Sparsity — 4× in this paper · explained in 4 episodes
• State Space Models — 4× in this paper
• Approximate Nearest Neighbor — 3× in this paper
• Audio Spectrogram Transformer — 3× in this paper
• Consistency — 3× in this paper · explained in 3 episodes
• Fisher Information — 3× in this paper · explained in 3 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Non-Homogeneous Poisson Process — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• Fast Fourier Transform — 2× in this paper
• Hypergraph — 2× in this paper · explained in 2 episodes
• Micro Video Popularity Prediction — 2× in this paper
• Quantization — 2× in this paper · explained in 12 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alternation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Concept Drift — 1× in this paper · explained in 4 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Dynamic Modeling — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Linear Time-Invariance — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Mean Absolute Error — 1× in this paper
• Objective Function — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• State Space Models (SSMs) — 1× in this paper · explained in 4 episodes
• Support Vector Regression — 1× in this paper
• Transformer Models — 1× in this paper · explained in 2 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• The Faiss library
• Transformer Feed-Forward Layers Are Key-Value Memories
• Contextual LSTM (CLSTM) models for Large scale NLP tasks
• AST: Audio Spectrogram Transformer
• Multi-modal and Metadata Capture Model for Micro Video Popularity Prediction
• A Content-Driven Micro-Video Recommendation Dataset at Scale

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