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

• URL: http://arxiv.org/abs/2201.05231v1
• Date: Thu, 13 Jan 2022 22:06:10 GMT
• Title: Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)
• Authors: Alexandra Iacob, Bogdan Cautis, Silviu Maniu
• Abstract summary: We study an influence problem in which little is assumed to be known about the diffusion network or about the model that determines how information may propagate. In this setting, an explore-exploit approach could be used to learn the key underlying diffusion parameters, while running the campaign. We describe and compare two methods of contextual multi-armed bandits, with upper-confidence bounds on the remaining potential of influencers.
• Score: 73.59962178534361
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by scenarios of information diffusion and advertising in social media, we study an influence maximization problem in which little is assumed to be known about the diffusion network or about the model that determines how information may propagate. In such a highly uncertain environment, one can focus on multi-round diffusion campaigns, with the objective to maximize the number of distinct users that are influenced or activated, starting from a known base of few influential nodes. During a campaign, spread seeds are selected sequentially at consecutive rounds, and feedback is collected in the form of the activated nodes at each round. A round's impact (reward) is then quantified as the number of newly activated nodes. Overall, one must maximize the campaign's total spread, as the sum of rounds' rewards. In this setting, an explore-exploit approach could be used to learn the key underlying diffusion parameters, while running the campaign. We describe and compare two methods of contextual multi-armed bandits, with upper-confidence bounds on the remaining potential of influencers, one using a generalized linear model and the Good-Turing estimator for remaining potential (GLM-GT-UCB), and another one that directly adapts the LinUCB algorithm to our setting (LogNorm-LinUCB). We show that they outperform baseline methods using state-of-the-art ideas, on synthetic and real-world data, while at the same time exhibiting different and complementary behavior, depending on the scenarios in which they are deployed.

Related papers

• 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)
• Potential Field Based Deep Metric Learning [8.670873561640903]
  Deep metric learning involves training a network to learn a semantically meaningful representation space. We present a novel, compositional DML model inspired by electrostatic fields in physics. We show that such decay helps improve performance on real world datasets with large intra-class variations and label noise.
  arXiv  Detail & Related papers  (2024-05-28T20:10:06Z)
• Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data [102.16105233826917]
  Learning from preference labels plays a crucial role in fine-tuning large language models. There are several distinct approaches for preference fine-tuning, including supervised learning, on-policy reinforcement learning (RL), and contrastive learning.
  arXiv  Detail & Related papers  (2024-04-22T17:20:18Z)
• BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction [40.226752807366864]
  We show that prompt-tuning methods fail to generalize to specific few-shot patterns. We propose BayesPrompt to learn prompts that contain the domain discriminative information. Our method achieves state-of-the-art performance on benchmarks.
  arXiv  Detail & Related papers  (2024-01-25T13:20:47Z)
• Targeted Advertising on Social Networks Using Online Variational Tensor Regression [19.586412285513962]
  We propose what we believe is the first contextual bandit framework for online targeted advertising. The proposed framework is designed to accommodate any number of feature vectors in the form of multi-mode tensor. We empirically confirm that the proposedUCB algorithm achieves a significant improvement in influence tasks over the benchmarks.
  arXiv  Detail & Related papers  (2022-08-22T22:10:45Z)
• Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization [53.11458949694947]
  We consider an adaptive version of content-dependent online influence problem where seed nodes are sequentially activated based on realtime feedback. Our algorithm maintains a network model estimate and selects seed adaptively, exploring the social network while improving the optimal policy optimistically.
  arXiv  Detail & Related papers  (2022-06-29T18:17:28Z)
• Uplifting Bandits [23.262188897812475]
  We introduce a multi-armed bandit model where the reward is a sum of multiple random variables, and each action only alters the distributions of some of them. This model is motivated by marketing campaigns and recommender systems, where the variables represent outcomes on individual customers. We propose UCB-style algorithms that estimate the uplifts of the actions over a baseline.
  arXiv  Detail & Related papers  (2022-06-08T18:00:56Z)
• PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting [63.84828478688975]
  We propose a novel perspective-aware approach called PANet to address the perspective problem. Based on the observation that the size of the objects varies greatly in one image due to the perspective effect, we propose the dynamic receptive fields (DRF) framework. The framework is able to adjust the receptive field by the dilated convolution parameters according to the input image, which helps the model to extract more discriminative features for each local region.
  arXiv  Detail & Related papers  (2021-10-31T04:43:05Z)
• Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network [58.05473757538834]
  This paper proposes a novel adversarial scoring network (ASNet) to bridge the gap across domains from coarse to fine granularity. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance.
  arXiv  Detail & Related papers  (2021-07-27T14:47:24Z)

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.