# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 1 Introduction  
Recommendation systems have become the heart of internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, and Amazon. Recommendation systems provide suggestions for items that can be of use to a user. These suggestions are aimed at supporting users in various decision-making processes, such as what items to buy, what music to listen to, or what news to read [1; 2; 3]. Pattern mining consists of discovering interesting, useful, and unexpected patterns in databases through tasks such as association rule mining, frequent pattern mining, and sequential pattern mining [4]. These data mining tasks are generally used by recommendation systems to generate a meaningful representation and learning of historical user purchase data. This work focuses on systems that mine sequential patterns of customer purchase history for purpose of making recommendations in the e-commerce application domain. Different types of recommendation systems accept different input data through explicit rating feedback (e.g., Table 1) and implicit derived feedback. Explicit feedback can be in the form of collecting ratings of products or text comments by users through registration forms/asking explicitly for interests and preferences, where users select numeric values from a specific evaluation system (e.g., a five-star rating system) to specify their likes and dislikes with respect to different items. Implicit feedback includes behaviors such as purchase history, browsing history, search patterns, time spent on specific pages, links followed by a user, button clicks, and user data from social network platforms. For example, the simple act of a user buying or browsing an item can be viewed as an endorsement of that item. Such forms of feedback are commonly used by online merchants such as Amazon.com [1]. A sample user-item rating matrix consisting of inputdata instance of a movie recommendation site (Table 1) is an example of explicit feedback information. Each cell in Table 1 is the rating value (preference) of a user for a movie on a 5-point scale (i.e., from 1 to 5), and the preferences marked with a question mark '?' are the missing or unknown values that need to be predicted. Consider a user's click and purchase behavior data, as shown in Table 2; this sample user's click and purchase behavior indicates that the customer ended up purchasing few items from the list of clicked items. Now, an implicit user's transaction (binary) user-item purchase matrix (Table 3) is created by analyzing the list of items purchased by the user; a value of 1 is assigned for the purchased items, while 0 represents non-purchased items by the same user. Analyzing users' implicit preferences (i.e., the behavior pattern data) has been used widely and has proven to be useful in practice for constructing input user-item matrix when explicit rating information on items is not available or needs to be made more informative by integrating more learned historical customer purchase behavior. Sequential pattern mining (SPM) discovers interesting subsequences as patterns (sequential patterns) in a sequence database that can be used later by end users or management to find associations between different items or events in their data for purposes such as marketing campaigns, business reorganization, prediction, and planning in the domain of E-commerce. A sequence database stores a number of records, where all records are sequences \(\{s_{1},s_{2},\ldots,s_{n}\}\) that are arranged with respect to time [4]. A sequence database can be represented as a tuple \(<\)SID, sequence-item sets\(>\), where SID represents the sequence identifier and sequence-item sets specify the sets of items (purchased, watched, etc.) enclosed in parenthesis () in the time order (such as every day, week, month) they are purchased by the SID. An example sequence database is retail customer transactions or purchase sequences in a grocery store, showing the collection of store items purchased every week for each customer for one month.