# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 1 Introduction
Thus, the user-item rating matrix can be extremely sparse and with low quality input data, i.e., less informative rating data not reflecting (1) how much a user likes a purchased item with value 1, (2) how frequently \begin{table}
\begin{tabular}{c l l} \hline \hline
**CustomerID** & **PurchasedItems** & **Timestamp** \\ \hline
01 & Bread, Milk & 10 Sep. 2019 00:48:44 \\ \hline
02 & Bread & 11 Sep. 2019 10:48:44 \\ \hline
01 & Bread, Milk, Sugar & 15 Sep. 2019 10:48:44 \\ \hline
02 & Sugar, Tea & 16 Sep. 2019 09:48:44 \\ \hline
01 & Milk & 18 Sep. 2019 00:48:44 \\ \hline
01 & Tea, Sugar & 19 Sep. 2019 00:48:44 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Historical purchase data. \begin{table}
\begin{tabular}{c l} \hline \hline
**SID** & **Sequences** \\ \hline
01 & \(<\) (Bread, Milk), (Bread, Milk, Sugar), (Milk), (Tea, Sugar) \(>\) \\ \hline
02 & \(<\) (Bread), (Sugar, Tea) \(>\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sequential database from historical purchase data. or how long ago a user purchased an item, or (3) what quantity of a product was purchased. One way to improve the input data is to integrate explicit rating with implicit rating drawn from historical purchase or click stream data, or alternatively to use learning algorithms such as sequential pattern mining (SPM) of historical purchase and click stream data to extract more informative customer purchase and click stream data behavior. This can be integrated into the user-item rating matrix, helping to reduce data sparsity and improve recommendation quality and accuracy. SPM can capture customer purchase behavior over time using mined sequential patterns; this is is crucial, as the time interval between items is useful for learning the time at what the next item might be purchased. The next purchase decision of a user is often influenced by their recent behaviors, and this approach considers the temporal preferences of users as a sequence of purchased items. An example frequent sequential pattern (FSP) that can be mined from a relevant E-Commerce purchase historical sequential database is \(<(milk, bread)\), \((milk, cream)>\). This indicates that it is generally learned from the historical purchase database that whenever customers buy milk and bread together in one week, they come back in the following week to buy milk and cream together. This sequential rule can be written as (milk, bread) \(\rightarrow\) (milk, cream). With a sequential rule such as this one, several of the unknown ratings in the input user-item rating matrix in Table 6 can be filled such that all users who have purchased the antecedent items (milk, bread) have a higher chance (say, 0.5 or a more specific determined chance value) of purchasing cream next. With this information, the ratings for users 1, 2, and 4 for cream can be changed from unknown to 0.5. In this way, a sequential pattern can be used to improve the quantity of rating values by providing possible values for missing/unrated items. A user-item purchase frequency matrix can then be constructed in which each value represents the quantity of a product purchased by a user. This purchase frequency is then normalized to a scaled value (0 to 1) representing how interested a user is in one item as compared to other items, improving the rating quality. If these historical sequential purchase patterns of a user are analyzed and integrated into the user-item matrix input, the rating quality (specifying level of interest or value for already rated items) and quantity (finding possible ratings for previously unknown ratings) can be enhanced and improved using the mined sequential patterns. Thus, the recommendation quality can be improved in terms of accuracy, scalability, and novelty. An important task for e-commerce sites is to make predictions about what users might buy in the future based on that user's history of shopping.