## 3 Our Approach
### Fusion Module  
The fusion module workflow is detailed in Figure 1. Firstly, the user interest features from the long-term interest model are divided into four equal parts. This was inspired by [PERSON] et al. [35], who suggested that user behavior consists of short sessions with varying interest points. After segmentation, multiple local features are cross-fused with short-term interest features through a shared attention mechanism, capturing the correlation between short-term features and local features. The aim is to identify the correlation between short-term feature fusion and local features to account for differences between long-term interests and short-term interests. \[a_{i}=\frac{exp(f(p_{s}^{u},p_{l}^{u,i}))}{\sum_{j=1}^{n}exp(f(p_{s}^{u},p_{l} ^{u,j}))} \tag{2}\]  
\[att(p_{l}^{u,j},p_{s}^{u})=a_{i}p_{l}^{u,i} \tag{3}\]  
\(p_{l}^{u,i}\) refers to the ith feature part equally divided into \(n\) parts. \(f\) is the fusion enhancement function. \(a_{i}\) calculates the attention fraction between the short-term and local features. The \(att\) is obtained as a result of fusion of individual local features with short-term features. The short-and long-term interest model incorporates an attention mechanism that calculates weights between historical serial goods and target goods, providing a direct integration of global information while ignoring one-sided interest. Meanwhile, the attention module in the fusion module leverages shared parameters to calculate weights between each local feature and short-term features, thereby avoiding an overly large number of parameters. While this approach can effectively integrate local features with short-term features and obtain part of the global long-term interest features, it still lacks the ability to fully integrate global information. To address this limitation, we propose the use of an MLP and sigmoid to calculate the weights of each interaction feature, and then stitch all interaction features together as user interest features. Multiple output feature vectors contain the evolution direction of user interest, and effectively integrating them is a crucial problem. Inspired by the channel attention mechanism in SE-Net for images, we propose a lightweight fusion unit that treats each feature as a channel. This unit dynamically adjusts the weights using the attention mechanism. Unlike the SDM model, our fusion unit is computationally efficient and adheres to the plug-and-play principle. The fusion process can be expressed as follows:  
\[p_{out}=o_{u}+o_{u}\cdot\sigma(f(maxpool(p^{u})+avgpool(p^{u}))) \tag{4}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DIN Model** & **User Histories** & \multicolumn{2}{c}{**Sequences**} & \multicolumn{2}{c}{**Categories**} & \multicolumn{2}{c}{**Time Span**} \\ \hline
**Length** & **Thread** & \(\leq\)**5** & \(\geq\)**50** & \(\leq\)**2** & \(\geq\)**20** & \(\leq\)**1** & \(\geq\)**3300** \\ \hline \multirow{4}{*}{10} & class & 31 & 34 & 2 & 67 & 41 & 39 \\  & popu & 0.016 & 0.005 & 0.000 & 0.008 & 0.010 & 0.005 \\  & cross & 0.027 & 0.333 & 0.384 & 0.176 & 0.015 & 0.034 \\ \hline \multirow{4}{*}{20} & class & 67 & 87 & 52 & 41 & 47 & 64 \\  & popu & 0.010 & 0.008 & 0.004 & 0.016 & 0.032 & 0.012 \\  & cross & 0.027 & 0.276 & 0.014 & 0.208 & 0.040 & 0.120 \\ \hline \multirow{4}{*}{40} & class & 145 & 165 & 164 & 144 & 201 & 193 \\  & popu & 0.014 & 0.005 & 0.007 & 0.010 & 0.003 & 0.005 \\  & cross & 0.064 & 0.202 & 0.014 & 0.096 & 0.026 & 0.082 \\ \hline \multirow{4}{*}{50} & class & 189 & 98 & 202 & 89 & 141 & 133 \\  & popu & 0.018 & 0.001 & 0.008 & 0.010 & 0.009 & 0.012 \\ \cline{1-1}  & cross & 0.025 & 0.330 & 0.011 & 0.173 & 0.028 & 0.085 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of prediction results of different length sequence models for different users. “Class” represents the number of categories, “popu” represents the proportion of popular items, and “cross” represents the proportion of recommended results crossed with historical items categories.