دانلود رایگان ترجمه مقاله داده کاوی در شبکه های اجتماعی برای تبلیغات هدفمند در بخش خودرو (نشریه IEEE 2017)

داده کاوی در شبکه های اجتماعی برای تبلیغات هدف در بخش خودروسازی

Data Mining on Social Networks for Target Advertisement in Automobile Sector

Abstract

There is huge potential to predict hidden social ties between different people sharing common area of interest or common friends. This paper presents data analysis on social network data for target advertising of the automobiles. The interrelated groups are derived from client’s interaction data in social networks. Based on these cohesive subgroups, the likelihood of client’s preference of a product category is inferred. Using this information, we construct a targeted advertising system. The experimental results on the social network dataset yields better quality of advertisement.

1 Introduction

The importance of advertising using client¶s feedback has long been acknowledged by businesses [9]. A lot of attention is attracted towards this research area for many reasons. There is a huge amount of product/service information available to clients. Hence, it is desirable for having different ways with which clients can wade through information that could help them find product/services. Secondly, it is important to understand what clients (both current and potential) needs, as a part of client relationship management (CRM). If one could identify the needs of clients more accurately and efficiently, they can advertise the products and services in a better way. It would increase the retention power of the company for its clients, the growth of the company and its profits.

Social data mining helps the organizations in targeting the consumer using behavioral and sentimental approach [25]. There are various popular social networking sites such as Facebook, Twitter, Instagram and Google+. People are also Connected to each other through various E-commerce sites [26]. Companies need to quantify and classify the feedback to make different business strategies by understanding emerging trends in a virtual world. [2].

This analysis is useful both for marketers as well as the clients. On one hand, it helps clients to understand popular preferences about a product or service. It is particularly useful for companies in evaluating the online feedback related to their products. These ideas and opinions propagating on a social network have been studied to improvise products and design new strategies of viral marketing [10], [11],[12][24].