# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 2 Existing Sequential Pattern-Based E-Commerce Recommendation Systems
### Segmentation-Based Approach: LiuRec09 [26]  
Summary of LiuRec09 System [26]: A hybrid recommendation system which combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method was proposed in [26]. An Example Application of LiuRec09 System:  
Assume E-commerce historical purchase data containing purchase items, with frequency of purchase, price, and transaction time as input. The Segmentation-based Sequential Rule (SSR) method would go through the following steps. Step 1: Customer clustering. Customers are clustered into distinct groups based on their RFM values (Recency, Frequency, and Monetary). The RFM patterns of each cluster are identified by assigning \(\uparrow\) or \(\downarrow\) according to whether the RFM value of a cluster is larger than or smaller than the overall average RFM value. Clusters with the same pattern are combined into one cluster. For example, clusters 3, 4, and 5 in Table 15 have the same pattern; similarly, clusters 2, 7, and 8 can be merged. Therefore, eight customer clusters can be reduced to four customer segments: loyal, potential, uncertain, and valueless, as based on their RFM patterns shown in Table 16. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **No. of Customers** & **R (Recency)** & **F (Freq-uency)** & **M (Monetary)** & **Patterns** \\ \hline Cluster 1 & 104 & 72.260 & 19.587 & 40,797.23 & R\(\uparrow\), F\(\downarrow\), M\(\uparrow\) \\ \hline Cluster 2 & 43 & 119.558 & 3.791 & 7342.326 & R\(\uparrow\), F\(\downarrow\), M\(\downarrow\) \\ \hline Cluster 3 & 17 & 64.294 & 67.2351 & 147,315.6 & R\(\downarrow\), F\(\uparrow\), M\(\uparrow\) \\ \hline Cluster 4 & 214 & 56.696 & 19.832 & 40,279.53 & R\(\downarrow\), F\(\uparrow\), M\(\uparrow\) \\ \hline Cluster 5 & 78 & 57.192 & 37.846 & 74,045.92 & R\(\downarrow\), F\(\uparrow\), M\(\uparrow\) \\ \hline Cluster 6 & 367 & 58.335 & 9.632 & 18,677.27 & R\(\downarrow\), F\(\downarrow\), M\(\downarrow\) \\ \hline Cluster 7 & 126 & 92.246 & 7.286 & 14,853.89 & R\(\uparrow\), F\(\downarrow\), M\(\downarrow\) \\ \hline Cluster 8 & 240 & 73.892 & 8.496 & 16,109.99 & R\(\uparrow\), F\(\downarrow\), M\(\downarrow\) \\ \hline Average & & 68.216 & 14.324 & 28,638.3 & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: K-means clusters based on the normalized RFM values. Step 3: Mining customer behavior from transaction clusters. To mine customer behavior according to purchase time, an association rule [44] is adopted for determining the most frequent pattern with confidence. From Table 17, a sequential rule \(Ap2\to Ep3\) (0.4,1) is extracted, with support of 40 percent and confidence of 100 percent. According to this rule, if a customer's purchase behavior in period P2 is in transaction cluster A, then their behavior in P3 will be in transaction cluster E. The other sequential rules \(Bp2\to Ep3\) (0.2,1) and \(Bp1\to Dp3\) (0.2,1) can be obtained similarly. Step 4: Determine the cluster sequences of target customers and match them. The degree of matching between a target customer's buying behavior and a sequential rule is calculated by a fitness measure. Step 5: Recommendation. Finally, the frequency count of each item in the predicted transaction cluster is calculated and the top N items with the highest frequency counts are returned. Segmentation-based KNN-CF method (SKCF). In this step, the Pearson's correlation coefficient for each customer is used to measure the similarity between the target customer and other customers in the same segment, then the k most similar (highest ranked) customers are selected as the k-nearest neighbors of the target customer. The N most frequent products not yet purchased by the target customer u in period T are selected as the top-N recommendations. Hybrid recommendation method. SSR and SKCF are combined linearly with a weighted combination, as shown below, where \(\alpha\) and \((1-\alpha)\) are the weights of SKCF and SSR methods, respectively.