Budgeted Coupon Advertisement Problem: Algorithm and Robust Analysis

Guo, Jianxiong; Chen, Tiantian; Wu, Weili

doi:10.1109/tnse.2020.2964882

Cited by 14 publications

(5 citation statements)

References 35 publications

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“…Usually, a product may have multiple features and the information about each feature may spread from one customer to another. Before formulating the problem formally, we first introduce the diffusion model about multiple information propagation on the social network [31], which are called multiple information diffusion (MID) Model.…”

Section: B Multiple Information Diffusion Modelmentioning

confidence: 99%

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Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks

Chen¹,

Guo²,

Wu³

2020

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Online social network has been one of the most important platforms for viral marketing. Most of existing researches about diffusion of adoptions of new products on networks are about one diffusion. That is, only one piece of information about the product is spread on the network. However, in fact, one product may have multiple features and the information about different features may spread independently in social network. When a user would like to purchase the product, he would consider all of the features of the product comprehensively not just consider one. Based on this, we propose a novel problem, multi-feature budgeted profit maximization (MBPM) problem, which first considers budgeted profit maximization under multiple features propagation of one product.Given a social network with each node having an activation cost and a profit, MBPM problem seeks for a seed set with expected cost no more than the budget to make the total expected profit as large as possible. We consider MBPM problem under the adaptive setting, where seeds are chosen iteratively and next seed is selected according to current diffusion results. We study adaptive MBPM problem under two models, oracle model and noise model. The oracle model assumes conditional expected marginal profit of any node could be obtained in O(1) time and a (1 − 1/e)-approximation algorithm is proposed. Under the noise model, we estimate conditional expected marginal profit of a node by reverse influence sampling technique and propose an efficient algorithm, which could return a (1 − exp( − 1))-approximation solution with high probability. Experimental results on three real-world datasets show efficiencies and superiorities of our algorithm.

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Section: B Multiple Information Diffusion Modelmentioning

confidence: 99%

“…Most of existing papers related to IM problem only consider a single diffusion, that is, only one piece of information about the product is spread on social networks. Some paper like [7], [31], [9], [29] indeed consider multiple diffusions of products. But the diffusions are for multiple products and each diffusion is for one product.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks

Chen¹,

Guo²,

Wu³

2020

Preprint

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“…Tong et al [24] considered the coupon allocation in the PM problem, and designed efficient randomized algorithms to achieve (1/2−ε)approximation with high probability. Guo et al [25] proposed a budgeted coupon problem whose domain is constrained and provided a continuous double greedy algorithm with a valid approximation. However, in our model, the formulation of competitiveness and definition of benefit are different from one of the above works.…”

Section: Related Workmentioning

confidence: 99%

Overall Evaluations on Benefits of Influence When Disturbed by Rivals

Guo,

Zhang,

2020

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Influence maximization (IM) is a representative and classic problem that has been studied extensively before. The most important application derived from the IM problem is viral marketing. Take us as a promoter, we want to get benefits from the influence diffusion in a given social network, where each influenced (activated) user is associated with a benefit. However, there is often competing information initiated by our rivals diffusing in the same social network at the same time. Consider such a scenario, a user is influenced by both my information and my rivals' information. Here, the benefit from this user should be weakened to certain degree. How to quantify the degree of weakening? Based on that, we propose an overall evaluations on benefits of influence (OEBI) problem. We prove the objective function of the OEBI problem is not monotone, not submodular, and not supermodular. Fortunately, we can decompose this objective function into the difference of two submodular functions and adopt a modular-modular procedure to approximate it with a data-dependent approximation guarantee. Because of the difficulty to compute the exact objective value, we design a group of unbiased estimators by exploiting the idea of reverse influence sampling, which can improve time efficiency significantly without losing its approximation ratio. Finally, numerical experiments on real datasets verified the effectiveness of our approaches regardless of performance and efficiency.

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“…Based on IC and LT model, many variant diffusion models have been proposed to simulate more practical and complicated scenarios [43], [44], [45], [46], [47], [48], [49], which still have some limitations. Therefore, understanding how the information is diffused on the networks and which factor can make the information spread successfully on the networks, and predicting the size of the popularity are challenging but important in many practical applications, like advertising [50], [51], viral marketing [52], [53], and recommendation [54].…”

Section: Introductionmentioning

confidence: 99%

Graph Representation Learning for Popularity Prediction Problem: A Survey

Chen,

Guo,

2022

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The online social platforms, like Twitter, Facebook, LinkedIn and WeChat, have grown really fast in last decade and have been one of the most effective platforms for people to communicate and share information with each other. Due to the "word of mouth" effects, information usually can spread rapidly on these social media platforms. Therefore, it is important to study the mechanisms driving the information diffusion and quantify the consequence of information spread. A lot of efforts have been focused on this problem to help us better understand and achieve higher performance in viral marketing and advertising. On the other hand, the development of neural networks has blossomed in the last few years, leading to a large number of graph representation learning (GRL) models. Compared with traditional models, GRL methods are often shown to be more effective. In this paper, we present a comprehensive review for recent works leveraging GRL methods for popularity prediction problem, and categorize related literatures into two big classes, according to their mainly used model and techniques: embedding-based methods and deep learning methods. Deep learning method is further classified into six small classes: convolutional neural networks, graph convolutional networks, graph attention networks, graph neural networks, recurrent neural networks, and reinforcement learning. We compare the performance of these different models and discuss their strengths and limitations. Finally, we outline the challenges and future chances for popularity prediction problem.Impact Statement-Social platforms have exploded in popularity in last decade, and information diffusion on social networks has attracted widespread attention in both industrials and academics. Companies need estimate whether marketing strategies will be successful before real implementation to save costs and earn more profits. On the other hand, it is vital to predict diffusion of misinformation before it spreads too far, and block its further diffusion. Therefore, it is important to understand underlying diffusion dynamics of cascade and its popularity trend. With recent achievements of graph representation learning in artificial intelligence and other fields, many works leveraged graph representation learning methods for information diffusion modeling and popularity prediction. This article presents a comprehensive review of recent works using graph representation learning methods for popularity prediction. Reviewing different methods can help researchers identify strengths and limitations of existing models, and find out challenges and directions for popularity prediction in future work.

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Budgeted Coupon Advertisement Problem: Algorithm and Robust Analysis

Cited by 14 publications

References 35 publications

Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks

Adaptive Multi-Feature Budgeted Profit Maximization in Social Networks

Overall Evaluations on Benefits of Influence When Disturbed by Rivals

Graph Representation Learning for Popularity Prediction Problem: A Survey

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