# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 2 Existing Sequential Pattern-Based E-Commerce Recommendation Systems  
The main aim of e-commerce websites is to turn their visitors into customers. As transaction data provide sets of preferred items and can be used to predict future customer preferences, researchers have applied association rule mining techniques to extract sequences in order to improve the performance of recommendation systems [30; 31]. However, such systems incorporate customer transaction data from only a single temporal period, which omits the dynamic nature of a customer's access sequences. Unlike association rules, sequential patterns [8] may suggest that a user who accesses a new item in the current time period is likely to access another item in the next time period. Thus, SPM techniques have been used for extracting complex sequential patterns of user purchase behavior and if these patterns are learned and included in the user-item matrix input, the accuracy of the recommendation system is improved, as the input becomes more informative before it is fed to CF. Thus, integrating CF and SPM of historical purchase data can improve recommendation quality, reduce data sparsity, and increase the novelty of recommendations. While sequential pattern mining algorithms such as (GSP [5], SPADE [32], and PrefixSpan [33]) mine frequent patterns from sequence database (e.g., of historical purchases or clicks) using a priori-like logic [34], Collaborative Filtering algorithms [1] follow a general four-step logic, to predict missing ratings in the input user-item rating matrix. First, the mean rating is computed for each user u. Second, the similarity between a target user v and all the other users u is computed using a similarity function such as the Pearson correlation coefficient. Third, user v's peer group is computed for each of v's unrated items (that is, v's top similar users). Finally, the predicted rating of user v is computed as the weighted average of the ratings of v's top K similar users for the unrated item.  
Existing E-commerce recommendation systems that can be found in the literature which have combined CF with some form of historical purchase sequences (SPM) to recommend items to users include the following ten systems: (1) model-based approach, ChoRec05 [35], (2) pattern segmentation framework, ChoRec09 [36], (3) sequential pattern-based collaborative recommender system, HuaRec09 [37], (4) segmentation-based approach, LiuRec09 [38], (5) hybrid online product recommendation, ChoiRec12 [39], (6) hybrid model (HM), RecSys16 [40], (7) product recommendation system (PRS), RecSys16 [41], (8) sequential pattern-based recommender system, SainiRec17 [42], (9) historical purchase and click stream-based recommendation, HPCRec18 [43], and (10) historical sequential pattern recommendation, HSPCRC19 [6]. A brief overview of these systems is provided next. Each reviewed system has a summary of its algorithm and methodology presented first before an example application of the system's algorithm to assist with clarity of the technique, where applicable for most systems.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**User/Item** & **Milk** & **Bread** & **Butter** & **Cream** & **Cheese** & **Honey** \\ \hline User 1 & 1 &? & 2 & 1 & 0.5 & 1 \\ \hline User 2 & 0.5 &? & 1 & 0.5 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Rich user-item frequency matrix with sequential rule.