# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 2 Existing Sequential Pattern-Based E-Commerce Recommendation Systems
### Sequential Pattern-Based Collaborative Recommender System: HuaRec09 [37]  
Summary of HuaRec09 [37] System: [PERSON] et al. (2009) proposed a hybrid recommendation system that uses a sequential pattern-based collaborative recommender system to predict the customer's time-variant purchase behavior in an E-commerce environment where the customer's purchase patterns may change gradually. A two-stage recommendation process is developed to predict customer purchase behavior for the product categories, as well as for product items. The time window weight is introduced to provide higher importance on the sequential patterns closer to the current time period that possess a larger impact on the prediction than patterns relatively far from the current time period. Considering all the target customer's transactional sequences in the current time period T and the previous number r periods \(T-1,T-2,\ldots,T-r\), the active customers most likely to purchase items in the next time period T + 1 (target prediction period) are determined. The proposed system consists of model training for the target customers and model use (implementation) for the active customers. Active customers are selected from the target customer to receive recommendations during model use. The steps in each of these modules are discussed below. Model training for the target customers goes through the four steps of:  
1. Identifying the target customers: the target customers can be identified according to customer behavioral variables such as recency, frequency, and monetary expenditure (RFM model) [45].
2. Building dynamic customer profile: dynamic customer buying behaviors can be modeled by analyzing a customer's periodic transaction data.
3. Clustering the customers: the customers are clustered based on their dynamic customer profiles using a genetic algorithm-based clustering approach.
4. Sequential pattern mining for each cluster: a cluster's sequential patterns represent the buying behavior of the customers in that cluster. The proposed sequential pattern-based prediction using the product categories involves generating a customer purchase sequence for each customer and discovering the sequential patterns for each cluster using an SPM algorithm such as GSP [5] or PrefixSpan [33].  
For model use for the active customera two-stage recommendation process is followed using cluster selection for the active customer, which includes predicting the top-M product categories and recommending the top-N product items. The top-M product categories are predicted based on the value of the product Category Recommendation Score (CRS). The CRS for the predicted \(category_{i}\) is calculated as follows:\[CRS^{category_{i}}=\sum_{period_{t}}(CATEGORY-SUPPOR_{period_{t}}^{category_{i}}\times WEIGHT _{period_{t}})\text{ for }T=T_{0},T_{1},\ldots,T_{r}\]  
where \(WEIGHT_{period_{t}}\) is the time window weight in \(period_{t}\).Top-N product items recommendation: the possible top-N items that the active customer will probably purchase in the target period are generated by calculating the recommendation score for each item in the top-M product categories. The Item Recommendation Score (IRS) for an item among the top-M product categories is calculated as follows:  
\[IRS^{item_{j}}=\sum_{period_{t}}(Purchase-Frequency_{period_{t}}^{item_{j}} \times Weight_{period_{t}})\text{ for }T=T_{0},T_{1},\ldots,T_{r}\]  
where \(Weight_{period_{t}}\) is the time window weight in \(period_{t}\) and \(Purchase-Frequency_{period_{t}}^{item_{j}}\) is the frequency of \(item_{j}\) bought by all customers in the same cluster in \(period_{t}\). The purchase frequency is defined as the number of times, rather than the of quantity, of purchases during a certain period. The top-N items with larger recommendation scores, excluding items bought by the active customer before, are then recommended to the active customer.