A two-level Product Recommender for E-commerce Sites by Using Sequential Pattern Analysis

Shahram Jamali, Yahya Dorostkar Navaei


With the development of communication networks and rapid growth of their applications, huge amount of information have been produced. Major part of these information are in electronic stores, and hence it's really hard to find desired products inside huggermugger. Product Recommendation System (PRS) tries to solve this problem by giving appropriate and fast recommendations to the customers. This paper proposes a two-level product recommender for E-commerce sites. At first, the available products are clustered by using C-Means algorithm to create groups of products with similar characteristics. Then, the second level considers the customers’ behavior and their purchase history for drawing the relationships between products by using Sequential Pattern Analysis (SPA) method. These relationships, eventually, will lead to appropriate recommendation for customers and also increases the likelihood of selling related products in electronic transactions. Extensive numerical simulations over UCI transactions 10k dataset indicates that 87% of records in mined sequential patterns are predicted correctly and the accuracy of recommendations is more than other RPSs.


Product recommendation system, two-level RPS, e-commerce, clustering, sequential pattern analysis

Full Text:


Copyright (c) 2016 International Journal of Integrated Engineering

Copyright International Journal of Integrated Engineering (IJIE) 2013.

ISSN : 2229-838X

e-ISSN : 2600-7916


Creative Commons License
This OJS site and its metadata are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.