Advertising competitions in social networks

Masucci, Antonia Maria; Silva, Alonso

doi:10.23919/acc.2017.7963668

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Artificial Intelligence is the branch of Computer science which focuses the development of intelligent machines [3] . It is otherwise called as Machine Intelligence.…”

Section: Artificial Intelligencementioning

confidence: 99%

Click Prediction for Advertisement in Websites using Linear Regression

Ramya¹

2021

TURCOMAT

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Prediction is one of the most powerful and effective method used nowadays for improvement in business. Machine Learning Algorithms plays a vital role in predicting the future of business. It is widely used in the field of Marketing and Advertising fields also. The Commercial Value for the advertisement is gained based on the user click on the website. Digital advertisement and marketing play very important role in influencing the profit of business. Many Machine Learning algorithms were used for predicting and analyzing the online advertisement. In this paper, Linear Regression is used for predicting the user click on the advertisement.

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“…Artificial Intelligence is the branch of Computer science which focuses the development of intelligent machines [3] . It is otherwise called as Machine Intelligence.…”

Section: Artificial Intelligencementioning

confidence: 99%

Click Prediction for Advertisement in Websites using Linear Regression

Ramya¹

2021

TURCOMAT

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“…They also used the voter model with discrete states for their diffusion dynamics. Later in [13], they extended the problem to the case of more than two players and presented the voter model with ranking scores. In both works, the considered action space was continuous, meaning that any fraction of the budget could be allocated to a node.…”

Section: Related Workmentioning

confidence: 99%

“…The above equation comes directly from the scenario and is common in other budget allocation studies [12,13]. The assignments in function f (.)…”

Section: Proposed Frameworkmentioning

confidence: 99%

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Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection

Ansari,

Dadgar,

Hamzeh

et al. 2019

Preprint

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Today, many companies take advantage of viral marketing to promote their new products, and since there are several competing companies in many markets, Competitive Influence Maximization has attracted much attention. Two categories of studies exist in the literature. First, studies that analyze the problem of which nodes from the network to select considering the existence of the opponents. Second, studies that focus on the problem of budget allocation. Although these studies have improved the problem in many aspects, their considered scenario is still incomplete. In this paper, we integrate these two lines of researches and propose a much more realistic scenario for the Competitive Influence Maximization problem. In our scenario, competition happens in two phases. First, parties identify the most influential nodes of the network. Then they compete over only these influential nodes by the amount of budget they allocate to each node. Also, despite the common assumption in all previous budget allocation studies that the action space is continuous, we consider the action space to be discrete. This assumption is more connected to real-world applications and significantly changes the problem. We model the scenario as a game and propose a novel framework for calculating the Nash equilibrium. Notably, building an efficient framework for this problem with such a huge action space and also handling the stochastic environment of influence maximization is very challenging. To tackle these difficulties, we devise a new payoff estimation method and a novel best response oracle to boost the efficiency of our framework.

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Adaptive Competition-Based Diversified-Profit Maximization with Online Seed Allocation

Yang²,

Gao³

2022

Algorithmic Aspects in Information and Management

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Advertising competitions in social networks

Cited by 9 publications

References 30 publications

Click Prediction for Advertisement in Websites using Linear Regression

Click Prediction for Advertisement in Websites using Linear Regression

Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection

Adaptive Competition-Based Diversified-Profit Maximization with Online Seed Allocation

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