# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 3 Taxonomy for Sequential Pattern-Based E-Commerce Recommendation Systems
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Rec System** & **Improving Rating Quality** & **Improving Rating Quantity** \\ \hline ChoRec05 & No use of historical purchases or clickstream data, no rating quality improvement & Association rule mining used to predict purchases \\ \hline ChenRec09 & RFM (Recency, Frequency, and Monetary) used to improve rating quality & Modified-Apriori algorithm used to predict purchases \\ \hline HuangRec09 & No use of historical purchases or clickstream data, no rating quality improvement & Association rule mining used to predict purchases \\ \hline LiuRec09 & RFM (Recency, Frequency, and Monetary) used to improve rating quality & Modified Apriori algorithm used to predict purchases \\ \hline ChoiRec12 & Use of historical, frequency of purchases, relative preference to improve rating quality & Association rule mining used to predict purchases \\ \hline Hybrid Model RecSys16 & Use of clicks, collect add-to-cart, payment, etc., to improve rating quality & Sequential pattern mining with Prefix-Span algorithm used to predict purchases \\ \hline Product RecSys16 & Use of historical and frequency of purchases to improve rating quality & Sequential pattern mining with FreeSpan used to predict purchases \\ \hline SainiRec17 & Use of historical purchases to improve rating quality & Sequential pattern mining with Spade used to predict purchases \\ \hline HPCRec18 & Use of historical, frequency click stream, and consequential bond of purchases to improve rating quality & Analyzes session-based data mined with consequential bonds to predict purchases even for items with no ratings \\ \hline HSPRec19 & Use of historical, frequency click stream, and consequential bond of purchases to improve rating quality & Sequential pattern mining with GSP and consequential bond used to predict purchases even for items with no ratings \\ \hline \hline \end{tabular}
\end{table} 
Table 39: How the surveyed recommendation systems improve the quality and quantity of the input rating matrix. Scalability: it has become increasingly easy to collect large number of ratings and implicit feedback information from various users in recent years. In such cases, the size of the dataset continues to increase over time. As a result, it has become increasingly essential to design recommendation systems that can perform effectively and efficiently in the presence of large amounts of data. Table 40 shows the effect of SP on the performance of the surveyed recommendation systems by examining and comparing how all the surveyed algorithms handle problems such as sparsity, novelty, and scalability while improving the user-item (U-I) rating quality and quantity of recommendation systems through the use of sequential patterns. The interpretation of the terms high, medium, and low in Table 40 with respect to the individual functionalities is defined below, followed by an explanation as to why these systems are within the specified range. Reducing Data Sparsity  
Low: no use of SPM; instead, association rule mining is used. Medium: uses SPM, and cannot integrate any other implicit user behaviors, such as clickstream data. High: uses SPM and integrates additional behavioral data such as clickstream data to enhance the user-item matrix. Improving Novelty  
Low: previously purchased items by the target user are included in the recommendation list. High: known items are excluded from being recommended, and associated products to purchased products are used for recommendation purposes to make the customer aware of potentially related products. Improving Scalability  
Low: no clustering technique is used to reduce the dimensionality of the dataset. Medium: a clustering technique is used to reduce the dimensionality of the dataset. High: a clustering technique is used along with an additional dimensionality reduction technique.