# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 2 Existing Sequential Pattern-Based E-Commerce Recommendation Systems
### Historical Sequential Pattern Recommendation: HSPRec19 [6]
Thus, to find purchase information for user 3, the relationship between clicks and purchases is analyzed by considering their sequence and recommending items based on the click sequential rule for cases where the user clicks and then does not purchase anything. Step 5: Compute the Click Purchase Pattern (CPS) similarity using the frequency and sequence of click and purchase patterns. If there is no purchase from among the with clicked item, use the recommended item. Step 6: Assign a Click Purchase (CPS) similarity value to the purchase patterns present in the consequential bond. Step 7: Assign weighted purchase patterns to the Weighted Frequent Purchase Pattern Miner (WFPP) and compute weights for the items present in the weighted purchase pattern using the equation below. \[R_{item_{i}}=\sum_{i=1}^{n}CPS_{item_{i}}/Support_{item_{i}}\]  
Step 8: Use the weights of the items to construct the rich user-item matrix. Step 9: Normalize the rich user-item purchase frequency matrix (Table 37) to obtain the normalized quantitatively rich user-item matrix (Table 38) using the unit normalization function below. \[Normalized\ r_{ui}=r_{ui}/\sqrt{r_{ui_{1}}^{2}+r_{ui_{2}}^{2}+\ldots+r_{ui_{n}} ^{2}}\]  
In [6], user-based collaborative filtering was used to compare and evaluate the performance of the ChoiRec12, HPCRec18, and HSPRec19 recommendation systems against the traditional CF algorithm in terms of the quality of ratings predictions with respect to the Mean Absolute Error (MAE) predictive accuracy metric by varying the number of users and nearest neighbors. The MAE compares the predicted ratings to actual user ratings over a test sample in a recommendation system, and is defined as the average absolute difference between predicted ratings and actual ratings. User-based collaborative filtering was used to compare and evaluate the performance of the ChoiRec12 [39], HPCRec18 [43], and HSPRec19 [6] recommendation systems against the traditional CF algorithm in terms of the quality of ratings prediction with respect to the Mean Absolute Error (MAE) predictive accuracy metric by varying the number of users and nearest neighbors. MAE compares the predicted ratings to actual user ratings over a test sample in a recommendation system, and is defined as the average absolute difference between predicted ratings and actual ratings. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**User/** & **Milk** & **Bread** & **Butter** & **Cream** & **Cheese** & **Honey** \\
**Item** & & & & & & \\ \hline User 1 & 1 &? & 2 & 1 & 1 & 1 \\ \hline User 2 &? &? & 1 & 1 & 2 & 1 \\ \hline User 3 &? &? &? &? &? &? \\ \hline \hline \end{tabular}
\end{table} 
Table 36: Rich user–item frequency matrix with sequential rules. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**User/** & **Milk** & **Bread** & **Butter** & **Cream** & **Cheese** & **Honey** \\
**Item** & & & & & & \\ \hline User 1 & 1 &? & 2 & 1 & 1 & 1 \\ \hline User 2 &? &? & 1 & 1 & 2 & 1 \\ \hline User 3 & 0.63 &? & 0.61 & 0.63 & 0.56 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 37: Rich user–item purchase frequency matrix. The performance of SP-based E-commerce recommendation systems such as ChoiRec12 [39], HPCRec18 [43], and HSPRec19 [6] was evaluated in terms of the quality of recommendations generated by varying the number of users with respect to classification accuracy measures such as the precision and recall, which evaluate the frequency with which the system makes correct/incorrect decisions. Precision is the fraction of all recommended items that are relevant, while recall is the fraction of all relevant items that are recommended.