# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 2 Existing Sequential Pattern-Based E-Commerce Recommendation Systems
### Pattern Segmentation Framework: ChenRec09 [36]  
Summary of ChenRec09 [36] System: [PERSON] et al. [36] proposed a sequential pattern-based recommender system that incorporates the RFM (Recency, Frequency, and Monetary) concept. \"Recency\" represents the length of the time period since the last purchase, with a lower value corresponding to a higher probability of the customer making repeat purchases. \"Frequency\" denotes the number of purchases within a specified time period, with a higher frequency indicating stronger customer loyalty. \"Monetary\" means the amount of money spent in this specified time period; thus, if a customer has a higher monetary value, the company should focus more resources on retaining that customer. RFM sequential patterns are then defined and a novel algorithm, named RFM-Apriori, is used to generate all RFM sequential patterns from customer's purchase data. The algorithm was developed by modifying the well-known a priori GSP sequential pattern mining algorithm [5], and consists of iterative phases.  
The RFM-Apriori Algorithm goes through Candidate generation phase: first, where the algorithm places all itemsets into the candidate-1 set \(C_{1}\), the set of candidate patterns with length 1, then scans the database to find the frequent (large) 1-patterns (\(L_{1}\)) from the candidate itemsets \(C_{1}\). An itemset is used as a unit to expand the patterns, rather than just an item, as it can reduce the number of phases needed to complete the algorithm, thereby improving efficiency. Second, supposing that the set of frequent (k\(-1\))-patterns \(L_{k-1}\) is already known, it is joined with itself apriori-gen join way to generate candidate RF  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rules** & **T-2** & **T-1** & **T** & **Support** & **Confidence** \\  & **(Jan)** & **(Feb)** & **(Mar)** & & \\ \hline
1 & 9 & - & 8 & 0.3 & 0.667 \\ \hline
3 & 9 & 2 & 8 & 0.1 & 1.0 \\ \hline
4 & 9 & 1 & 2 & 0.1 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Sample derived association rules.  
patterns of length k, where \(k\geq 2\) if they have the same (k\(-\)2)-postfix. The algorithm scans the database to determine the supports of the patterns in \(C_{k}\), then finds \(L_{k}\) by removing those patterns from \(C_{k}\) that have support lower than the minimum support threshold. This iteration is repeated by increasing k by one until no more patterns can be generated. To count supports, an inverse candidate tree is used to store all candidate patterns in \(C_{lk}\), where a leaf node corresponds to a candidate pattern. Support values can be accumulated in each leaf node using every data sequence to traverse the tree. This is an efficient method of determining whether a candidate pattern satisfies the recency constraint. This traversal procedure is a recursive program by which all subsequences in T can be matched with all candidate patterns in \(C_{lk}\). If a matched subsequence can be found that satisfies both the recency and monetary constraints for a pattern (leaf node), the rfm-support and rf-support of this pattern is increased by one. If it satisfies only the recency constraint, however, only the rf-support is increased by one. Using RFM-Apriori algorithm, a pattern segmentation framework is proposed which allows for partitioning the RFM-patterns into segments relevant to the RFM criteria in order to generate valuable information on customer purchasing behavior for managerial decision-making. By partitioning the patterns into groups based on the RFM indices, a retailer can further compare, contrast, and aggregate these groups of patterns to find possible changes in purchasing patterns over time.