Context-aware Advertisement Modeling and Applications in Rapid Transit Systems

• URL: http://arxiv.org/abs/2409.09956v1
• Date: Mon, 16 Sep 2024 02:59:36 GMT
• Title: Context-aware Advertisement Modeling and Applications in Rapid Transit Systems
• Authors: Afzal Ahmed, Muhammad Raees,
• Abstract summary: We present an advertisement model using behavioral and tracking analysis. We present a model using the agent-based modeling (ABM) technique, with the target audience of rapid transit system users to target the right person for advertisement applications.
• Score: 1.342834401139078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In today's businesses, marketing has been a central trend for growth. Marketing quality is equally important as product quality and relevant metrics. Quality of Marketing depends on targeting the right person. Technology adaptations have been slow in many fields but have captured some aspects of human life to make an impact. For instance, in marketing, recent developments have provided a significant shift toward data-driven approaches. In this paper, we present an advertisement model using behavioral and tracking analysis. We extract users' behavioral data upholding their privacy principle and perform data manipulations and pattern mining for effective analysis. We present a model using the agent-based modeling (ABM) technique, with the target audience of rapid transit system users to target the right person for advertisement applications. We also outline the Overview, Design, and Details concept of ABM.

Related papers

• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse. Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• A Deep Learning Approach for Imbalanced Tabular Data in Advertiser Prospecting: A Case of Direct Mail Prospecting [0.6990493129893112]
  We propose a supervised learning approach for identifying new customers, i.e., prospecting, which comprises how we define labels for our data and rank potential customers. This framework is designed to tackle large imbalanced datasets with vast number of numerical and categorical features. Our framework comprises two components: an autoencoder and a feed-forward neural network.
  arXiv  Detail & Related papers  (2024-10-02T01:19:40Z)
• Packaging Up Media Mix Modeling: An Introduction to Robyn's Open-Source Approach [0.7373617024876725]
  Open-source computational package Robyn is designed to facilitate the adoption of m/MMM for digital advertising measurement. This article explores the computational components and design choices that underpin Robyn. As a widely adopted and actively maintained open-source tool, Robyn is continually evolving.
  arXiv  Detail & Related papers  (2024-03-08T17:46:03Z)
• InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
  Reward hacking, also termed reward overoptimization, remains a critical challenge. We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective. We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
  arXiv  Detail & Related papers  (2024-02-14T17:49:07Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Modeling the Telemarketing Process using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach [0.06906005491572399]
  This research aims at leveraging the power of telemarketing data in modeling the willingness of clients to make a term deposit. Real-world data from a Portuguese bank and national socio-economic metrics are used to model the telemarketing decision-making process.
  arXiv  Detail & Related papers  (2023-10-30T08:46:55Z)
• A Topical Approach to Capturing Customer Insight In Social Media [0.0]
  This research addresses the challenge of fully unsupervised topic extraction in noisy, Big Data contexts. We present three approaches we built on the Variational Autoencoder framework. We show that our models achieve equal to better performance than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-14T11:15:28Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• Challenges and approaches to privacy preserving post-click conversion prediction [3.4071263815701336]
  We provide an overview of the challenges and constraints when learning conversion models in this setting. We introduce a novel approach for training these models that makes use of post-ranking signals. We show using offline experiments on real world data that it outperforms a model relying on opt-in data alone.
  arXiv  Detail & Related papers  (2022-01-29T21:36:01Z)
• Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Framework [69.5850969606885]
  We propose a novel multi-task learning framework to improve the state of the art in micro-influencer ranking based on multimedia content. We show significant improvement both in terms of accuracy and model complexity. The techniques for ranking and interpretation presented in this work can be generalised to arbitrary multimedia ranking tasks.
  arXiv  Detail & Related papers  (2021-07-29T13:04:25Z)
• Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism [57.68557165836806]
  We propose a model to predict the trajectories of target agents around an autonomous vehicle. We encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents. To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20% error decrease.
  arXiv  Detail & Related papers  (2020-07-06T03:48:08Z)

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.