Targeted advertisement in social networks using recommender systems

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.