Survey on Web Mining Techniques and Challenges of E-commerce in Online Social Networks

Abstract

Objectives: In recent years, we have tremendous growth of users in Online Social Networks (OSN) such as Facebook, Google+, twitter etc. This becomes major reason for enabling web as largest market defining E-Commerce. Many companies use OSN as their sales channel as they can reduce operating cost for managing orders significantly compared with traditional channels. Also viral marketing is very popular in OSN. But the major drawback of this channel is if the company doesn’t satisfy the customer, then the same OSN can rapidly propagate a bad reputation of the company affecting the company’s business. Hence for customers it has become very important to identify and filter dishonest recommenders. So it becomes very important to recommend right items to right customers. In this survey, we aim to give a comprehensive review of research related to E-Commerce in OSN. Methods: First, we discuss web usage mining techniques for better shopping websites to satisfy customers. Also we discuss web mining techniques to find dishonest recommenders in OSN. Findings/ Improvements: Our survey explores the existing research highlights and also presents various researches in these topics. Also, we propose recommendation system which uses Semantic Web Mining process integrated with domain ontology which can be used to extract interesting patterns from, complex and heterogeneous data.