Abstract

Within the emergence of social web (web 2.0), new platform in technology named social networks, brought in to being. Social networks (SN) become more crowded and their rapidly growth caused scientists to search for methods analyzing the data which is implicated in social networks. Social network analysis with special attention to SN’s graph is a method that helps data extraction. These data could be used in targeted advertisements (Ad) which could impress users more. In the field of e-advertisements, presenting ads and sales are combined together using hypertexts or hypermedia to the nearest retailer or e-shops. So, targeted advertisement could be mentioned as an effective solution in the field of marketing on the web. Scientists have been focused on various variables and features that could be considered to target users in an appropriate way. While mentioning them, some new features are added. In this article, a framework has been proposed which facilitate targeted advertisements in social networks’ platform; using social networks information, previous advertisements and their status to have more precise information for recommender systems. Recommender system is used as a tool to target each user according to its preferences and interests. The main goal is to show the most effective advertisements in sidebar and attract users to share word of mouth (WOM) advertisements with each other. Considering user’s type through their activity in a social network and omitting repetitive advertisements ease our aim.

Introduction

Marketing is listed as a primary activity in Michel porter’s value chain model and it has a critical role in each business. Lots of researches have been performed for finding suitable methods of targeted advertising and making the most impression possible on people. This paper’s main goal is not only to fascinate customers, but also to make an improvement in the field of advertising. Reaching this aim would result in paying attention to new media such as social networks. Analyzing social networks would help to have exact information from any user through its profile, users’ interactions with others and generally their behavior. Social Networks (SN) users are considered as potential customers and having exact and precise information about each user, would guide us to recommend useful goods or services to them according to their own interests and tastes. This is a good solution to overcome data’s redundancies, which is the problem of information century.