# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 2 Existing Sequential Pattern-Based E-Commerce Recommendation Systems
### Product Recommendation System: PRS RecSys16 [41]  
[PERSON] and [PERSON] (2016) proposed a two-level product hybrid recommendation system which combines a C-Means clustering algorithm and Freespan algorithm. At first, the available products are clustered by using the C-Means algorithm to create groups of products with similar characteristics. Then, the second level considers the customer's behavior and their purchase history in order to draw relationships between products using Sequential Pattern Analysis (SPA). These relationships eventually lead to appropriate recommendations for customers and increase the likelihood of selling related products in electronic transactions.  
The PRS (Product Recommendation System) includes two levels of product recommendation; the first level is recommended before product purchase and the other after purchasing. PRS initially collects product data from the electronic store, separates the products according to their type, then clusters them based on their numerical attributes into three separate clusters of high, medium, and low quality using the C-means algorithm. Here, the C-Means clustering algorithm is used to separate products by their types and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item\_Id/ User\_id** & **562** & **529** & **267** & **858** & **241** \\ \hline
10001569 & & 2 & 4 & & 1 \\ \hline
100022999 & 1 & 1 & 2 & & \\ \hline
10000003 & & 1 & & & \\ \hline
100009489 & 3 & & 2 & 1 & 3 \\ \hline
100018271 & & 1 & & & \\ \hline
100020308 & & & 3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 21: [PERSON]’s item–user rating matrix.  
create groups with similar features to classify products. This algorithm generates clusters based on fuzzy logic, and does not consider sharp boundaries between the clusters, allowing each feature vector to belong to different clusters by a certain degree. The degree of membership between a feature vector and a cluster is usually considered as a function of its distance from the cluster centroid points, based on minimization of the following objective function:  
\[J_{m}=\sum_{i=1}^{N}\sum_{i=1}^{C}u_{ij}^{m}\|X_{i}-c_{j}\|^{2},1\leq m\leq\infty,\]  
where m is any real number greater than 1, \(u_{ij}\) is the degree of membership of \(x_{i}\) in cluster j, \(x_{i}\) represents the i-th d-dimensional measured data, \(c_{j}\) is the d-dimensional center of the cluster, and \(\|*\|\) is any norm expressing the similarity between any measured data and the center.  
Next, the PRS tries to identify the customer's requirements and criteria using an online form that takes information about product such as type, quality, price, brand, etc. This information is used to assign an appropriate cluster to the customer. In the second level, information about the history of the customer's shopping behavior is collected. This information is used to explore the relations between products using the Freespan algorithm of the SPA method. Freespan mines sequential patterns by partitioning the search space and projecting the sequence subdatabases recursively based on the projected itemsets [46]. Eventually, these relations and patterns are provided as product recommendations, recommending products associated with the purchased products, making the customer aware of potentially related products, as relationships between products increase the likelihood of a customer buying those products together.