# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 3 Fflstma
### Attention Mechanism  
The theoretical principle of the attention mechanism in streaming movie prediction model is mainly to focus on key information and improve the model performance by assigning different weights to different features and time series information.  
The model's LSTM output sequence is \(LSTM_{out}=[h_{1},h_{2},\ldots,h_{t},\ldots,h_{T}]\), where \(h_{t}\) represents the hidden state of dimension \(d\) (\(h_{t}\in\mathbb{R}^{d}\)) at time step \(t\).  
First, this model uses \(LSTM_{out}\) as an input to calculate the attention distribution for the production of Query, Key, and Value in the attention mechanism. A query vector \(Q\):\(Q=W_{q}LSTM_{out}\) is generated by a linear layer \(W_{q}\) (dimension \(d\times d\)).  
Similarly, the key vector \(K\):\(K=W_{k}LSTM_{out}\) is generated by a linear layer \(W_{k}\) (dimension \(d\times d\)).  
And the value vector \(V\):\(V=W_{v}LSTM_{out}\) is generated by the linear layer \(W_{v}\) (dimension is \(d\times d\)).  
Then, attention scores are calculated using Scaled Dot-Product Attention. The attention score \(e_{t}\) is calculated as follows:  
\[e_{t}=\frac{QK^{T}}{\sqrt{d}} \tag{4}\]  
where \(QK^{T}\) denotes the matrix multiplication of the transpose of the query vector \(Q\) and the key vector \(K\), and \(\sqrt{d}\) is a scaling factor used to prevent the gradient from disappearing or exploding due to too large an attention score.  
Next, the attention scores are then standardized using the softmax algorithm to obtain attention weights:  
\[\alpha_{t}=\frac{exp(e_{t})}{\sum_{t=1}^{T}exp(e_{t})} \tag{5}\]  
Finally, the attended output \(attended_{LSTM}\) is then obtained by weighting and adding the value vector \(V\) in accordance with the attention weights:  
\[attended_{LSTM}=\sum_{t=1}^{T}\alpha_{t}V_{t} \tag{6}\]  
In this streaming movie prediction model, in addition to applying the attention mechanism to the LSTM output, other features (e.g., the holiday feature \(H\) is transformed to \(f_{H}(H)\) by the linear layer \(f_{H}\), the movie's own characteristic influence factor \(M\) is transformed to \(f_{M}(M)\) by \(f_{M}\), the PCA time series downscaling score \(P\) is transformed to \(f_{P}(P)\) by \(f_{P}\), and the whether-platform-only feature \(E\) is transformed into \(f_{E}(E)\) by \(f_{E}\).  
Then, feature fusion is performed, and the fused feature vector is:  
\[F=\begin{bmatrix}attended_{LSTM}\\ f_{H}(H)\\ f_{M}(M)\\ f_{P}(P)\\ f_{E}(E)\end{bmatrix} \tag{7}\]  
Finally, the completely connected layer \(f_{final}\) maps the fused features to the expected output, so that the predicted value \(y=f_{final}(F)\).  
The adaptive weighting architecture enables the context-sensitive modulation of temporal and cross-feature dependencies across input dimensions, effectively prioritizing critical temporal patterns through learned relevance scoring. This design addresses the dynamic nature of streaming movie performance metrics by establishing responsive information filters that evolve with input variations, thereby enhancing predictive precision and robustness in modeling evolving revenue distribution patterns.