Near Optimal Strategies for Targeted Marketing in Social Networks

被引：0

作者：

Pasumarthi, Rama Kumar ^{[1
]}

Narayanam, Ramasuri ^{[2
]}

Ravindran, Balaraman ^{[3
]}

机构：

[1] Amer Express, Big Data Labs, Hyderabad, Telangana, India

[2] IBM Res, Hyderabad, Telangana, India

[3] Indian Inst Technol, Madras, Tamil Nadu, India

来源：

PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (AAMAS'15) | 2015年

关键词：

Targeted marketing; Social networks; Submodular functions; Seed selection; Greedy approximation algorithms;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we address the problem of Targeted Influence Maximization (TIM) through a social network. Often companies want to promote their products to certain type of customers as opposed to targeting the entire social network. That is, there is a need to maximize influence over a targeted audience in the network. Towards this end, we present a novel objective function for the targeted influence maximization problem. It turns out that this objective function is the difference between two relevant submodular functions. By building upon the recently developed theory for optimizing the difference between two submodular functions, we develop an efficient algorithm with provable guarantees. We show that the quality of solution for TIM improves using our proposed approach, when compared over a standard baseline.

引用

页码：1679 / 1680

页数：2