Cost-aware Targeted Viral Marketing in billion-scale networks
Online social networks have been one of the most effective platforms for marketing and advertising. Through the “world-of-mouth” exchanges, so-called viral marketing, the influence and product adoption can spread from few key influencers to billions of users in the network. To identify those key influencers, a great amount of work has been devoted for the Influence Maximization (IM) problem that seeks a set of k seed users that maximize the expected influence. Unfortunately, IM encloses two impractical assumptions: 1) any seed user can be acquired with the same cost and 2) all users are equally interested in the advertisement. In this paper, we propose a new problem, called Cost-aware Targeted Viral Marketing (CTVM), to find the most cost-effective seed users who can influence the most relevant users to the advertisement. Since CTVM is NP-hard, we design an efficient (1 - 1/√e-ϵ - e)-approximation algorithm, named BCT, to solve the problem in billion-scale networks. Comparing with IM algorithms, we show that BCT is both theoretically and experimentally faster than the state-of-the-arts while providing better solution quality. Moreover, we prove that under the Linear Threshold model, BCT is the first sub-linear time algorithm for CTVM (and IM) in dense networks. In our experiments with a Twitter dataset, containing 1.46 billions of social relations and 106 millions tweets, BCT can identify key influencers in each trending topic in only few minutes.
Code (1)
Tasks
MarketingSimilar Papers 제목 키워드 기반
Evidential positive opinion influence measures for viral marketing
The Viral Marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large casca…
MarketingDynamic Gradient Influencing for Viral Marketing Using Graph Neural Networks
The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use…
AttributeMarketingTrained Miniatures: Low cost, High Efficacy SLMs for Sales & Marketing
Large language models (LLMs) excel in text generation; however, these creative elements require heavy computation and are accompanied by a steep cost. Especially for targeted applications such as sales and marketing outr…
Text GenerationAI in Food Marketing from Personalized Recommendations to Predictive Analytics: Comparing Traditional Advertising Techniques with AI-Driven Strategies
Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. This paper explores the shift fro…
MarketingBandit Profit-maximization for Targeted Marketing
We study a sequential profit-maximization problem, optimizing for both price and ancillary variables like marketing expenditures. Specifically, we aim to maximize profit over an arbitrary sequence of multiple demand curv…
Marketing