With remarkable expansion of information through the internet, users prefer to receive the exact information they need through some suggestions to save their time and money. Thus, Recommendation Systems (RecSys) have become the heart of business strategies of E-commerce as they can increase sales and revenue as well as customer loyalty. RecSys techniques provide suggestions for items/products to be purchased, rented or used by a user. The most common type of recommendation system technique is Collaborative Filtering (CF), which takes user’s interest in an item (explicit rating) as input in a matrix known as the user-item rating matrix, and produces an output for unknown ratings of users for items from which top N recommended items for target users are defined. E-commerce recommendation systems usually deal with massive customer sequential databases such as historical purchase or click sequences. The time stamp of a click or purchase event is an important attribute of each dataset as the time interval between item purchases may be useful to learn the next items for purchase by users. Sequential Pattern Mining mines frequent or high utility sequential patterns from a sequential database. RecSys accuracy will be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user-item rating matrix input. Thus, integrating collaborative filtering (CF) and sequential pattern mining (SPM) of historical clicks and purchase data can improve recommendation accuracy, diversity and quality and this survey focuses on review of existing RecSys with the same goals exposing their methodologies, achievements, limitations, and potentials for solving more problems in this domain. 

 

This survey provides a systematic review on the importance of integrating SPM with CF for recommendation systems, before providing an in-depth comparative review of different Sequential pattern based collaborative E-commerce recommendation systems which have attempted to integrate historical purchase sequences (SPM) or sequences with CF to recommend items to users such as HuangRec09, LiuRec09, HOPE RecSys12,  Hybrid Model RecSys16, Product RecSys16,  HPCRec18,  HSPCRec19, SEERs20 exposing their impact and future prospects in the recommendation process.

 

 

Internal Reader: Dr. Dima Alhadidi

 

External Reader: Dr. Dilian Yang

 

Advisor:Dr. Christie Ezeife

 

 

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