Contextual Bandits for Maximizing Stimulated Word-of-Mouth Rewards

• URL: http://arxiv.org/abs/2606.15146v1
• Date: Sat, 13 Jun 2026 06:18:33 GMT
• Title: Contextual Bandits for Maximizing Stimulated Word-of-Mouth Rewards
• Abstract summary: optimizing stimulated word-of-mouth through social networks requires identifying and targeting connected users who are most susceptible to spillover.<n>We present a novel contextual multi-armed bandit framework that learns individual spillover probabilities and ranks connected users to maximize rewards from stimulated word-of-mouth.
• Score: 5.185131234265025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stimulated word-of-mouth is a strategy that promotes information sharing through prompts or incentives. Optimizing stimulated word-of-mouth through social networks requires identifying and targeting connected users who are most susceptible to spillover, a phenomenon where the influence of recommendations extends beyond the immediate audience to impact their connected users. The probability of spillover varies across individuals, and their connections, leading to heterogeneity. Understanding and accurately estimating the spillover probabilities among users in social networks is crucial for improving the effectiveness of stimulated word-of-mouth. To address this, we present a novel contextual multi-armed bandit framework that learns individual spillover probabilities and ranks connected users to maximize rewards from stimulated word-of-mouth. Experiments on real-world network datasets demonstrate that accounting for spillover heterogeneity enhances the targeting precision of top-$k$ connected users, boosting rewards and outperforming baseline methods that do not learn individual spillover effects.

Related papers

• Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior [0.9978961706999835]
  The spread of misinformation on social networks poses a significant challenge to online communities and society at large.<n>This paper seeks to mitigate the spread of misinformation by enabling proactive interventions, identifying and ranking users according to key behavioral indicators associated with harmful content dissemination.
  arXiv  Detail & Related papers  (2026-08-17T09:28:42Z)
• Edge-Only Universal Adversarial Attacks in Distributed Learning [49.546479320670464]
  In this work, we explore the feasibility of generating universal adversarial attacks when an attacker has access to the edge part of the model only. Our approach shows that adversaries can induce effective mispredictions in the unknown cloud part by leveraging key features on the edge side. Our results on ImageNet demonstrate strong attack transferability to the unknown cloud part.
  arXiv  Detail & Related papers  (2024-11-15T11:06:24Z)
• Influence Maximization via Graph Neural Bandits [54.45552721334886]
  We set the IM problem in a multi-round diffusion campaign, aiming to maximize the number of distinct users that are influenced. We propose the framework IM-GNB (Influence Maximization with Graph Neural Bandits), where we provide an estimate of the users' probabilities of being influenced.
  arXiv  Detail & Related papers  (2024-06-18T17:54:33Z)
• Leveraging heterogeneous spillover in maximizing contextual bandit rewards [10.609670658904562]
  We present a framework that allows contextual multi-armed bandits to account for such heterogeneous spillovers.<n>Our framework leads to significantly higher rewards than existing state-of-the-art solutions.
  arXiv  Detail & Related papers  (2023-10-16T10:34:41Z)
• Incentivized Communication for Federated Bandits [67.4682056391551]
  We introduce an incentivized communication problem for federated bandits, where the server shall motivate clients to share data by providing incentives. We propose the first incentivized communication protocol, namely, Inc-FedUCB, that achieves near-optimal regret with provable communication and incentive cost guarantees.
  arXiv  Detail & Related papers  (2023-09-21T00:59:20Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks [3.3946853660795884]
  We propose an end-to-end reinforcement learning-based framework, named Geometric Actor-Critic (GAC), to discover effective incentive allocation policies. We use three real-world social network datasets to evaluate the performance of the proposed GAC.
  arXiv  Detail & Related papers  (2022-03-17T19:41:49Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction [24.793013471521924]
  Incentivization has been proven to be a more proactive way to affect users' behaviors. We propose a novel algorithm for exploring influential users in unknown networks. We design an adaptive incentive allocation approach that determines incentive values based on users' preferences and their influence ability.
  arXiv  Detail & Related papers  (2021-07-13T11:20:10Z)
• CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks [14.695979686066062]
  We propose CLAIM - Curriculum LeArning Policy for Influence Maximization to improve the sample efficiency of RL methods. We conduct experiments on real-world datasets and show that our approach can outperform the current best approach.
  arXiv  Detail & Related papers  (2021-07-08T04:52:50Z)
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation [50.08581302050378]
  Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences. We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet) In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
  arXiv  Detail & Related papers  (2020-01-15T08:45:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.