## 5 Experimental Evaluation
### Baseline Methods  
We compare our method with the following recommendation approaches:  
* User-based collaborative filtering (User-CF) represents a classical paradigm in recommendation systems that generates personalized recommendations by leveraging user similarity patterns. The algorithm operates on a user-item rating matrix to identify users exhibiting similar preferences to the target user through similarity metrics, predominantly employing cosine similarity calculations. The system subsequently generates recommendations by suggesting items that have been positively rated by similar users but have not yet been interacted with by the target user. * Item-based collaborative filtering (Item-CF) represents an alternative collaborative filtering paradigm that generates recommendations by computing item-to-item similarities. This methodology calculates similarity metrics between items based on user interaction data, such as ratings and purchase history. The recommendation process involves analyzing the similarities between items that users have previously interacted with and potential items of interest, thereby generating personalized recommendations through item-to-item correlation patterns. * Matrix factorization (MF) is a model-based collaborative filtering technique that decomposes the user-item rating matrix to predict users' preferences for unrated items. Through this decomposition process, MF methods project both users and items into a shared low-dimensional latent space, generating dense vector representations that capture their inherent characteristics. These latent representations are then leveraged to estimate potential ratings for items that users have not yet interacted with, facilitating the generation of personalized recommendation lists. * Singular value decomposition (SVD), a specialized form of matrix factorization, is a fundamental technique for generating recommendations through the decomposition of user-item rating matrices. This method systematically decomposes the rating matrix into three constituent components: a user matrix, a diagonal singular value matrix, and an item matrix. By leveraging these decomposed matrices, the system \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**User ID** & **Product ID** & **Rating** & \
\begin{tabular}{c} **Vectorized** \\ **Comment** \\ \end{tabular} &
\begin{tabular}{c} **Vectorized Time** \\ **Vectorized Time** \\ \end{tabular} \\ \hline
10000110 & 1101001001 & 5 & \
\begin{tabular}{c} 0.45608386, \\ 0.11218067... \\ \end{tabular} &
\begin{tabular}{c} 0.6166, \(-0.7872\),... \\ \end{tabular} \\
101000100 & 101011001 & 5 & \
\begin{tabular}{c} 0.5078723, \(-0.049773\), \\ \end{tabular} &
\begin{tabular}{c} 0.5610, \(-0.8272\),... \\ \end{tabular} \\
101011111 & 11110101 & 5 & \
\begin{tabular}{c} 0.5526989, \\ \(-0.0165948,\)... \\ \end{tabular} &
\begin{tabular}{c} \(-0.9474\), \(-0.3200\),... \\ \end{tabular} \\
10001110 & 111000011 & 5 & \
\begin{tabular}{c} 0.520465, \(-0.0103036\),... \\ \end{tabular} &
\begin{tabular}{c} \(-0.4616\), \(-0.3560\),... \\ \end{tabular} \\
10111000 & 101001110 & 5 & \
\begin{tabular}{c} 0.48985627, \\ \(-0.235026,\)... \\ \end{tabular} &
\begin{tabular}{c} 0.8461, \(-0.6451\),... \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training set examples. \begin{table}
\begin{tabular}{c c c} \hline \hline
**User ID** & **Product ID** & **Rating** \\ \hline
11100111 & 101011001 & 5 \\
100100101 & 111100110 & 5 \\
1110001 & 101110010 & 5 \\
100111000 & 1000110111 & 3 \\
10000110 & 1010000001 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation set examples. can effectively predict users' potential ratings for previously unrated items and subsequently generate personalized recommendation lists. The mathematical rigor of SVD enables accurate capture of latent relationships between users and items, facilitating precise recommendation generation.