هدف گیری شبکه های اجتماعی در بازاریابی ویروسی
ترجمه نشده

هدف گیری شبکه های اجتماعی در بازاریابی ویروسی

عنوان فارسی مقاله: بهینه سازی چندمنظوره تکاملی برای هدف گیری نافذ شبکه های اجتماعی در بازاریابی ویروسی
عنوان انگلیسی مقاله: Evolutionary multiobjective optimization to target social network influentials in viral marketing
مجله/کنفرانس: سیستم های خبره با برنامه های کاربردی - Expert Systems with Applications
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: بازاریابی، مدیریت فناوری اطلاعات
کلمات کلیدی فارسی: بازاریابی ویروسی، بیشینه سازی نفوذ، هدف گیری نافذ، شبکه های اجتماعی، بهینه سازی چندمنظوره تکاملی، مدل سازی مبتنی بر عامل
کلمات کلیدی انگلیسی: Viral marketing، Influence maximization، Influentials targeting، Social networks، Evolutionary multiobjective optimization، Agent-based modeling
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.eswa.2020.113183
دانشگاه: Andalusian Research Institute DaSCI “Data Science and Computational Intelligence”, University of Granada, Granada 18071, Spain
صفحات مقاله انگلیسی: 36
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 5/891 در سال 2019
شاخص H_index: 162 در سال 2020
شاخص SJR: 1/190 در سال 2019
شناسه ISSN: 0957-4174
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14323
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- State of the art in viral marketing

3- The agent-based market simulation model

4- Multiobjective viral marketing optimization problem

5- Evolutionary multiobjective optimization methods for influentials selection

6- Experiments and analysis of results

7- Main conclusions and limitations of the study

References

بخشی از مقاله (انگلیسی)

Abstract

Marketers have an important asset if they effectively target social networks’ influentials. They can advertise products or services with free items or discounts to spread positive opinions to other consumers (i.e., word-of-mouth). However, main research on choosing the best influentials to target is single-objective and mainly focused on maximizing sales revenue. In this paper we propose a multiobjective approach to the influence maximization problem with the aim of increasing the revenue of viral marketing campaigns while reducing the costs. By using local social network metrics to locate influentials, we apply two evolutionary multiobjective optimization algorithms, NSGA-II and MOEA/D, a multiobjective adaptation of a single-objective genetic algorithm, and a greedy algorithm. Our proposal uses a realistic agent-based market framework to evaluate the fitness of the chromosomes by simulating the viral campaigns. The framework also generates, in a single run, a set of non-dominated solutions that allows marketers to consider multiple targeting options . The algorithms are evaluated on five network topologies and a real data-generated social network, showing that both MOEA/D and NSGA-II outperform the single-objective and the greedy approaches. More interestingly, we show a clear correlation between the algorithms’ performance and the diffusion features of the social networks.

Introduction

On-line social networks such as Facebook or Instagram make people (potential consumers) more connected than ever before. With just a few actions, consumers can instantly communicate their products and brands’ opinions. Social networks’ influentials have thousands of friends and a wordof-mouth process can create a cascade of positive or negative information about a brand. This word-of-mouth process in social networks is crucial for marketers and advertisers (Leskovec et al., 2007; Haenlein and Libai, 2017). In fact, people place more value in friends’ recommendations than in those from traditional advertisement channels such as TV. Viral marketing (VM) consists of targeting certain consumers to encourage a faster product’s adoption (Haenlein and Libai, 2017). The selection of these influentials is not a random but a complex optimization process that involves the analysis of the social network of consumers to trigger a large cascade of adoptions (known in the literature as influence maximization (IM) (Domingos and Richardson, 2001)) thus favoring a positive information diffusion. Designing the best VM strategies before running them in the real market is possible through the use of artificial social networks (Watts and Dodds, 2007) and simulations using paradigms such as agent-based modeling (ABM) (Epstein, 2006). ABM can describe and simulate networklevel interactions between consumers to reproduce word-of-mouth mechanisms that provide realism into marketing models and allow modelers to study emergent behaviors (Janssen and Jager, 2003; Epstein, 2006). These models enable the aggregation of individual-level interactions through the underlying artificial social network, creating high level outcomes and incorporating individual behavioral rules without higher level assumptions (Chica et al., 2018).