Reinforcement Learning in Social Media Marketing

2021 ◽  
pp. 30-48
Author(s):  
Patrik Eklund
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Action Selection ◽  
Social Media Marketing ◽  
Rule Bases ◽  
Facebook Marketing

In this chapter, the authors describe an architecture for reinforcement learning in social media marketing. The rule bases used for action selection within the architecture build upon many-valued (fuzzy) logic. Action evaluation and internal learning is based on neural network like structures. In using variables measuring the effect of advertising, we must understand direction of influence between advertiser, owning the content of the advertisement, and advertisee, as the target of an advertisement, and as facilitated by social media marketing. Examples are drawn from Facebook marketing.

Calculation of Facebook Marketing Effectiveness in Terms of ROI

2021 ◽  
pp. 600-625
Author(s):  
Tereza Semerádová ◽  
Petr Weinlich
Keyword(s):  
Social Media ◽  
Performance Assessment ◽  
Technical Aspect ◽  
Calculation Model ◽  
Social Media Marketing ◽  
Marketing Effectiveness ◽  
Analytical Information ◽  
Main Emphasis ◽  
Future Value ◽  
Facebook Marketing

This chapter demonstrates how to assess the performance of organic and sponsored activities on Facebook using the data available in Facebook Ads Manager, Facebook Page Insights, and Google Analytics. The main aim of the proposed ROI calculation model is to connect common social media marketing objectives with the analytical information available. The main emphasis is put on the technical aspect of ad performance assessment. The authors explain how the Facebook attribution system and post-impression algorithm work, describe the relation between advertising goals and metrics displayed as achieved campaign results, and demonstrate how to derive ROI indexes from different Facebook conversions. The chapter also includes a practical example how to calculate current and future value of ongoing ads.

scholarly journals Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning

2020 ◽  
Vol 28 (6) ◽  
pp. 1178-1189 ◽  
Author(s):  
Giacomo Capizzi ◽  
Grazia Lo Sciuto ◽  
Christian Napoli ◽  
Dawid Polap ◽  
Marcin Wozniak
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Probabilistic Neural Network ◽  
Lung Nodules

Extensive Use of Facebook Marketing by Entrepreneurs in Gaining a Wider Reach

2014 ◽  
Vol 6 (3) ◽  
pp. 53-62
Author(s):  
Ashok Kumar Wahi ◽  
Kunal Verma ◽  
Rati Vadehra
Keyword(s):  
Social Media ◽  
Business Development ◽  
Social Media Marketing ◽  
Customer Engagement ◽  
Integrated Marketing ◽  
Social Media Platform ◽  
Media Platform ◽  
Facebook Pages ◽  
Facebook Marketing

This paper focuses mainly on the entrepreneurial aspect of using Facebook pages as a platform for business development and customer engagement through it. It also discusses various Facebook pages features, its powerful analytics tool called insights and how entrepreneurs are leveraging this social media platform for their ventures and ultimately how social media marketing is largely impacting the integrated marketing of the entrepreneurs.

Calculation of Facebook Marketing Effectiveness in Terms of ROI

2019 ◽  
pp. 286-310 ◽  
Author(s):  
Tereza Semerádová ◽  
Petr Weinlich
Keyword(s):  
Social Media ◽  
Performance Assessment ◽  
Technical Aspect ◽  
Calculation Model ◽  
Social Media Marketing ◽  
Marketing Effectiveness ◽  
Analytical Information ◽  
Main Emphasis ◽  
Future Value ◽  
Facebook Marketing

This chapter demonstrates how to assess the performance of organic and sponsored activities on Facebook using the data available in Facebook Ads Manager, Facebook Page Insights, and Google Analytics. The main aim of the proposed ROI calculation model is to connect common social media marketing objectives with the analytical information available. The main emphasis is put on the technical aspect of ad performance assessment. The authors explain how the Facebook attribution system and post-impression algorithm work, describe the relation between advertising goals and metrics displayed as achieved campaign results, and demonstrate how to derive ROI indexes from different Facebook conversions. The chapter also includes a practical example how to calculate current and future value of ongoing ads.

The Impact of Social Media Marketing on Urban Youth’s Brand Loyalty: Through Facebook Marketing

2021 ◽  
Vol 4 (1) ◽  
pp. 10-19
Author(s):  
Tanveer Ahmed ◽  
Azrin Saeed
Keyword(s):  
Social Media ◽  
Urban Youth ◽  
Brand Loyalty ◽  
Negative Influence ◽  
Social Media Marketing ◽  
Night Time ◽  
Validated Questionnaire ◽  
Social Media Site ◽  
The Impact ◽  
Facebook Marketing

Social media being established as an effective medium of communication, marketers now highly focus on social media marketing create brand awareness and institute brand loyalty. Considering social media is the most popular among the urban youth of the country and Facebook being most popular social media site, this study is thus designed in order to identify the impact of Facebook marketing on urban youth’s brand loyalty.  Information was derived by conducting a validated questionnaire with a sample of 306 people which was further evaluated through regression analysis. It was found night time, 08:01 PM – 5:59 AM, is the most popular time period for the urban youth to use Facebook. Funny, informative, international/ national news contents are top preferred content on Facebook. A positive relationship between urban youth’s brand loyalty and components such as advantageous campaign, facebook group, popular contents and brand’s relevant contents of Facebook marketing. Whereas, updated content and electronic word of mouth (E-WOM) rather had a negative influence on the brand loyalty of the urban youth.

scholarly journals Social Media Marketing Optimization Method Based on Deep Neural Network and Evolutionary Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qing Bian
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Optimization Algorithm ◽  
Back Propagation ◽  
Optimization Methods ◽  
Optimization Method ◽  
Internet Technology ◽  
Social Media Marketing ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm

Under the background of the vigorous development of China’s market economy, the marketing mix is constantly updated, which promotes the all-round development of various industries. Social media marketing has formed a relatively solid theoretical and practical foundation, especially with the continuous updating and iteration of Internet technology and the improvement of people’s requirements for experience, and we must find ways to optimize the methods of social media marketing. This study mainly introduces several optimization methods of social media marketing based on deep neural networks and advanced algorithms, and the experiments of gradient-based back-propagation algorithm and adaptive Adam’s optimization algorithm show that the proposed optimization algorithm can easily achieve the global optimal state based on the combination of back-propagation algorithm and Adam’s optimization algorithm. Accuracy of marketing is very important, so we introduce a scheme of how to accurately market, and the scheme is effective. Firstly, the FCE model is constructed by a three-layer back-propagation neural network, and then, the data input layer is designed to achieve the effect of the model.

scholarly journals A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Na Guo ◽  
Caihong Li ◽  
Tengteng Gao ◽  
Guoming Liu ◽  
Yongdi Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Success Rate ◽  
Fuzzy Logic Control ◽  
Logic Control ◽  
Local Path Planning ◽  
Local Path

Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.

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