Beyond Adaptive Submodularity: Adaptive Influence Maximization with Intermediary Constraints

11/08/2019
by   Shatian Wang, et al.
0

We consider a brand with a given budget that wants to promote a product over multiple rounds of influencer marketing. In each round, it commissions an influencer to promote the product over a social network, and then observes the subsequent diffusion of the product before adaptively choosing the next influencer to commission. This process terminates when the budget is exhausted. We assume that the diffusion process follows the popular Independent Cascade model. We also consider an online learning setting, where the brand initially does not know the diffusion parameters associated with the model, and has to gradually learn the parameters over time. Unlike in existing models, the rounds in our model are correlated through an intermediary constraint: each user can be commissioned for an unlimited number of times. However, each user will spread influence without commission at most once. Due to this added constraint, the order in which the influencers are chosen can change the influence spread, making obsolete existing analysis techniques that based on the notion of adaptive submodularity. We devise a sample path analysis to prove that a greedy policy that knows the diffusion parameters achieves at least 1-1/e - ϵ times the expected reward of the optimal policy. In the online-learning setting, we are the first to consider a truly adaptive decision making framework, rather than assuming independent epochs, and adaptivity only within epochs. Under mild assumptions, we derive a regret bound for our algorithm. In our numerical experiments, we simulate information diffusions on four Twitter sub-networks, and compare our UCB-based learning algorithms with several baseline adaptive seeding strategies. Our learning algorithm consistently outperforms the baselines and achieves rewards close to the greedy policy that knows the true diffusion parameters.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/29/2022

Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization

Online influence maximization aims to maximize the influence spread of a...
research
11/08/2019

Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence

We propose a new class of Linear Threshold Model-based information-diffu...
research
01/18/2021

Buying Data Over Time: Approximately Optimal Strategies for Dynamic Data-Driven Decisions

We consider a model where an agent has a repeated decision to make and w...
research
06/19/2020

Multi-Round Influence Maximization

In this paper, we study the Multi-Round Influence Maximization (MRIM) pr...
research
10/03/2020

Neighborhood Matters: Influence Maximization in Social Networks with Limited Access

Influence maximization (IM) aims at maximizing the spread of influence b...
research
02/12/2022

Online Bayesian Recommendation with No Regret

We introduce and study the online Bayesian recommendation problem for a ...
research
01/13/2022

Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)

Motivated by scenarios of information diffusion and advertising in socia...

Please sign up or login with your details

Forgot password? Click here to reset