# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 2 Existing Sequential Pattern-Based E-Commerce Recommendation Systems
### Model-Based Approach: ChoRec05 [35]  
Summary of ChoRec05 [35]: A hybrid recommendation system that combines the Self-Organizing Map (SOM) clustering technique and association rule-based sequential cluster rules was proposed for mining the changes in customer buying behavior over time in [35]. The self organizing map (SOM) allows customer historical purchase transactions arranged in purchase time to be assigned a numbered cluster. A change in customer cluster number as a result of each transaction is used to create customer purchase sequences. Further, association rule mining is used to mine sequential patterns from the built customer sequences. The problem solved by [35] can be phrased as follows. For a given purchase sequence of a target customer c, based on a time period l (with l for example, being a period of every month or three months), for the past l-1 periods before time T, find the product p that the target customer will most likely buy at time T. The recommendation procedure is divided into two components, a model-building phase and a recommendation phase. An Example Application of ChoRec05 System [35]: The problem being solved is stated as: Given the input data set as: Input: historical purchase data in E-commerce dataset including customer ID, purchased items, and duration of transaction., when the expected output data sets are: Output: recommends products to each user., apply ChoRec05 algorithm to solve this recommendation task. This ChoRec05 [35] algorithm will start by going through the model building phase. Model-building phase: this phase is performed once to create a reliable model from the customer transaction database, which includes transaction clustering, where the transactions are transformed into an input matrix composed of a bit vector. These time-ordered vectors for a given customer represent the purchase history of that customer; the input matrix can be thought of as the customer's dynamic profile. Identification of cluster sequences: the cluster sequence of a customer is learned by identifying the cluster to which each transaction of the customer belongs during each time period (e.g., in January, February, March). Using the customer transaction change cluster database (e.g., Table 12, called loci in [35], association rule mining is used to predict customer movement. In Table 12, the first row means that a customer with CID 001 purchase pattern is in Cluster 9 in January (which is time T-2), then moved to Cluster 2 in February (time T-1), and then moved to Cluster 8 in time T (March). Extraction of sequential cluster rules: to mine customer behavior according to purchase time, the association rule \(R_{i}\)[44] is adopted for determining the most frequent rule patterns that equal or exceed a given minimum support and minimum confidence, as shown in the sample rule in Table 13. With association rule mining, in the first phase, frequent patterns are mined from the input database. Then, association rules are derived from these frequent patterns and only rules with confidence equal to or exceeding a set minimum confidence level are retained as being significant. The confidence of a rule is the number of times both the left and right sides of the rule (antecedent \(\rightarrow\) consequent) appear in the database divided by the number of times only the left side of the rule appears in the records of the database. A sample mined rules obtained from the customer transaction loci (movement) is shown in Table 13. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CID** & **T-2** & **T-1** & **T** \\  & **(Jan)** & **(Feb)** & **(Mar)** \\ \hline
001 & 9 & 2 & 8 \\ \hline
002 & 9 & 1 & 2 \\ \hline
003 & 2 & 9 & 3 \\ \hline
004 & 9 & - & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Purchase behavior loci of selected customers.