Internet Advertising Investment Analysis Based on Beijing and Jinhua Signaling Data

Xia, Song; Jiang, Hao; Zhang, Yi; Peng, Duo

doi:10.1109/cse/euc.2019.00084

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…To extract latent features from the raw data, we used a tensor decomposition technique that divides a tensor into smaller tensors or matrices. Tensor decomposition has been commonly used to extract latent features from data whose form is a tensor and has demonstrated its effectiveness in data analysis [ 28 , 29 ]. Among various methods for tensor decomposition, we used the Tucker decomposition because it is a generalized form of tensor decomposition [ 30 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Jung

Moon

Hwang

2020

IJERPH

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For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results.

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Section: Methodsmentioning

confidence: 99%

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Jung

Moon

Hwang

2020

IJERPH

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“…This paper proposes and investigates a new framework-the Federated Generative Adversarial Network and Clustering Aggregation (Fed-GANCC)-to address the challenges encountered during the rapid growth of the global internet advertising market. The growth rate of the advertising market has increased by approximately 20%, from $250 billion in 2017 to $430 billion in 2021 [1][2][3][4]. However, challenges such as privacy leaks, high communication costs, significant data discrepancies, and the formation of isolated data islands, non-identically independently distributed (non-IID) data, and concept drift persist during this growth.…”

Section: Introductionmentioning

confidence: 99%

Empowering precise advertising with Fed-GANCC: A novel federated learning approach leveraging Generative Adversarial Networks and group clustering

Su,

Wei,

Lei

et al. 2024

PLoS ONE

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In the realm of targeted advertising, the demand for precision is paramount, and the traditional centralized machine learning paradigm fails to address this necessity effectively. Two critical challenges persist in the current advertising ecosystem: the data privacy concerns leading to isolated data islands and the complexity in handling non-Independent and Identically Distributed (non-IID) data and concept drift due to the specificity and diversity in user behavior data. Current federated learning frameworks struggle to overcome these hurdles satisfactorily. This paper introduces Fed-GANCC, an innovative federated learning framework that synergizes Generative Adversarial Networks (GANs) and Group Clustering. The framework incorporates a user data augmentation algorithm predicated on adversarial generative networks to enrich user behavior data, curtail the impact of non-uniform data distribution, and enhance the applicability of the global machine learning model. Unlike traditional approaches, our framework offers user data augmentation algorithms based on adversarial generative networks, which not only enriches user behavior data but also reduces the challenges posed by non-uniform data distribution, thereby enhancing the applicability of the global machine learning (ML) model. The effectiveness of Fed-GANCC is distinctly showcased through experimental results, outperforming contemporary methods like FED-AVG and FED-SGD in terms of accuracy, loss value, and receiver operating characteristic (ROC) indicators within the same computing time. Experimental results vindicate the effectiveness of Fed-GANCC, revealing substantial enhancements in accuracy, loss value, and receiver operating characteristic (ROC) metrics compared to FED-AVG and FED-SGD given the same computational time. These outcomes underline Fed-GANCC’s exceptional prowess in mitigating issues such as isolated data islands, non-IID data, and concept drift. With its novel approach to addressing the prevailing challenges in targeted advertising such as isolated data islands, non-IID data, and concept drift, the Fed-GANCC framework stands as a benchmark, paving the way for future advancements in federated learning solutions tailored for the advertising domain. The Fed-GANCC framework promises to offer pivotal insights for the future development of efficient and advanced federated learning solutions for targeted advertising.

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“…Clustering is an unsupervised machine learning task. It involves automatically discovering natural grouping in the data [40].…”

mentioning

confidence: 99%

Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework

Viktoratos

Tsadiras

2021

Information

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This work conducts a systematic literature review about the domain of personalized advertisement, and more specifically, about the techniques that are used for this purpose. State-of-the-art publications and techniques are presented in detail, and the relationship of this domain with other related domains such as artificial intelligence (AI), semantic web, etc., is investigated. Important issues such as (a) business data utilization in personalized advertisement models, (b) the cold start problem in the domain, (c) advertisement visualization issues, (d) psychological factors in the personalization models, (e) the lack of rich datasets, and (f) user privacy are highlighted and are pinpointed to help and inspire researchers for future work. Finally, a design framework for personalized advertisement systems has been designed based on these findings.

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Internet Advertising Investment Analysis Based on Beijing and Jinhua Signaling Data

Cited by 4 publications

References 16 publications

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Empowering precise advertising with Fed-GANCC: A novel federated learning approach leveraging Generative Adversarial Networks and group clustering

Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework

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