# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 1 Introduction
This problem can be modeled using one of the most successful methods in the literature, the Collaborative Filtering (CF) technique, which makes use of explicit user rating-item matrix data from the user for the purpose of recommendation. The general technique of CF [1] accepts as its input an incomplete user-item rating matrix with the goal of predicting the unknown rating of a target user or item. For user-based CF, ratings of like-minded users of a target user u are used to make recommendations for user u. The weighted ratings of this group of neighbors are used as the predicted rating of the targeted user u. Similarly, functions can be computed between the rows of the ratings of item-user matrix to discover similar users likely to purchase the same types of products. A major advantage of this model is its ability to capture general taste for recommendation. However, this kind of algorithm has two \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**User Id/Products** & **Milk** & **Bread** & **Butter** & **Cream** & **Cheese** \\ \hline User1 & 1 & 1 & 1 &? & 1 \\ \hline User2 & 1 & 1 &? &? &? \\ \hline User3 & 1 &? &? & 1 & 1 \\ \hline User4 & 1 & 1 & 1 &? &? \\ \hline \hline \end{tabular}
\end{table} 
Table 6: An E-commerce user-item rating matrix. obvious shortcomings. First, the effectiveness of such algorithms are greatly reduced when the user's explicit rating behavior data is sparse; second, these methods ignore the time context of user behavior i.e., how the customer's purchase behavior may vary over time, and as such are unable to capture the sequential behavior of users. SPM techniques [7; 8] have been used alone recently to make recommendations more effective by extracting sequential patterns of user purchase behavior, as the user's next purchase is affected by their previous purchases and actions. This recommendation often utilizes a user's implicit feedback data; the major advantage of this model is its ability to capture users' sequential purchase behavior for recommendations. However, this SPM recommendation model alone cannot capture a user's general taste. It can be seen that both of these methods (CF and SPM) have shortfalls. In fact, both sequential behavior and users' general taste are important factors that influence user purchasing behavior, as indicated in [9; 10; 11]. This motivates a systematic review of the importance of integrating SPM with CF for recommendation systems to improve recommendation quality through more diverse recommendations, closing the high sparsity matrix problem, and thereby making recommendations better by taking into account users' general taste and sequential behavior. The review of these sequential pattern-based collaborative E-commerce recommendation systems involves comparison of their features, such as their recommendation accuracy, user-rating matrix input data sparsity ratio and functionalities (e.g., ability to recommend novel and diverse products, ability to scale up to frequently changing products, and user scalability), recommendation approaches, improving on understanding of the system's algorithms with example application of system through a clear example, and highlighting their strengths, weaknesses, and future prospects in the recommendation process. The focus of the survey research in this paper is on in-depth understanding of algorithmic methods for collaborative filtering system-based RS that enhance recommendation quality through sequential pattern mining of historical purchase and click stream data. Earlier work and surveys on collaborative filtering based techniques include [12; 13; 14; 15; 16; 17; 18]. This work is different from existing surveys or reviews of methods for evaluating recommendation systems, such as [19; 20], which provide a framework with no discussion of any algorithms.