# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 3 Taxonomy for Sequential Pattern-Based E-Commerce Recommendation Systems  
A taxonomy for existing SP-based E-commerce recommendation systems is proposed based on the two categories discussed below. Effect of Sequential Patterns on Recommendation Systems with Respect to Improving the Quality and Quantity of the User-Item Rating Matrix Input  
The user-item rating matrix is usually sparse, poorly informative, and leads to poor recommendations [28]. In order to capture more real-life customer purchase behavior and provide historical purchase relationships between already purchased items and recommended items, the historical sequential purchase patterns of users are analyzed and integrated into the user-item rating matrix input. This has the effect of enhancing and improving rating quality (specifying the level of interest or value for already-rated items) and quantity (finding possible ratings for previously unknown ratings) using mined sequential patterns. Table 39 shows how the surveyed recommendation systems can improve the quality and quantity of the user-item rating matrix input through the use of sequential patterns and compares them to each other. Effect of Sequential Patterns on Recommendation Systems with Respect to Handling the Problems of Sparsity, Novelty and Scalability  
In academic environments, the performance evaluation of recommendation systems is dominated by simulation-based experiments using datasets consisting of historical ratings or implicit feedback. The quality of the output of an algorithm can then be assessed with the help of accuracy metrics. Being able to accurately predict the relevance of items for users is a central problem in recommendation systems research. Therefore, increasing prediction accuracy is a relevant goal of research [47]. However, accuracy alone is not enough; recommending items that the user might have bought anyways is of little business value. Focusing on accuracy alone can lead to monotonous recommendations and limited discovery; thus, it is important for recommendation systems to assess multiple (possibly \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**User/** & **Milk** & **Bread** & **Butter** & **Cream** & **Cheese** & **Honey** \\
**Item** & & & & & & \\ \hline User 1 & 0.35 &? & 0.70 & 0.35 & 0.35 & 0.35 \\ \hline User 2 & 0.35 &? & 0.35 & 0.35 & 0.70 & 0.35 \\ \hline User 3 & 0.48 &? & 0.53 & 0.38 & 0.47 & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 38: Quantitative rich user–item purchase frequency matrix. competing) goals in parallel, for instance, handling data sparsity while improving novelty and scalability of the recommendation systems. Sparsity: in practice, many commercial recommendation systems (e.g., book recommendations on Amazon.com) are used to evaluate large product sets. In these systems, even active customers may have purchased only under 1% of the products (1% of two million books is 20,000 books), i.e., only a few of the total number of items available in a database are rated by users. Thus, in any recommendation system, the number of ratings already obtained is usually very low compared to the number of ratings that needs to be predicted. This results in a sparse user-item matrix, and generates weak or poor recommendations as a result of insufficient rating information. Novelty: the novelty evaluates the likelihood of a recommendation system in providing recommendations to the user that they are not aware of or that they have not seen in the past.