Geo-Spatial Market Segmentation &amp; Characterization Exploiting User Generated Text Through Transformers &amp; Density-Based Clustering

Ferro-Diez, Luis E.; Villegas, Norha M.; Diaz-Cely, Javier; Acosta, Sebastián

doi:10.1109/access.2021.3071620

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…Likewise, in their study, Ferro-Díez et al [ 15 ] delved into the potential of artificial intelligence-enabled location-based services for mobile advertising. They scrutinized various algorithms, including random forest, support vector machines, and artificial neural networks, aiming to construct an efficient visualization of geographic areas alongside their relevance scores for a predetermined set of categories.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Architecture for Federated Learning-Based Smart Advertising

Seyghaly,

Garcia,

Masip-Bruin

2024

Sensors

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This paper introduces a cutting-edge data architecture designed for a smart advertising context, prioritizing efficient data flow and performance, robust security, while guaranteeing data privacy and integrity. At the core of this study lies the application of federated learning (FL) as the primary methodology, which emphasizes the authenticity and privacy of data while promptly discarding irrelevant or fraudulent information. Our innovative data model employs a semi-random role assignment strategy based on a variety of criteria to efficiently collect and amalgamate data. The architecture is composed of model nodes, data nodes, and validator nodes, where the role of each node is determined by factors such as computational capability, interconnection quality, and historical performance records. A key feature of our proposed system is the selective engagement of a subset of nodes for modeling and validation, optimizing resource use and minimizing data loss. The AROUND social network platform serves as a real-world case study, illustrating the efficacy of our data architecture in a practical setting. Both simulated and real implementations of our architecture showcase its potential to dramatically curtail network traffic and average CPU usage, while preserving the accuracy of the FL model. Remarkably, the system is capable of achieving over a 50% reduction in both network traffic and average CPU usage even when the user count escalates by twenty-fold. The click rate, user engagement, and other parameters have also been evaluated, proving that the proposed architecture’s advantages do not affect the smart advertising accuracy. These findings highlight the proposed architecture’s capacity to scale efficiently and maintain high performance in smart advertising environments, making it a valuable contribution to the evolving landscape of digital marketing and FL.

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Section: Related Workmentioning

confidence: 99%

A Comprehensive Architecture for Federated Learning-Based Smart Advertising

Seyghaly,

Garcia,

Masip-Bruin

2024

Sensors

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“…Transformers are currently the dominant approach in natural language processing (NLP) [ 1 , 2 ]. Since their introduction in 2017 [ 3 ], they have been successfully applied in various areas of NLP, including semantic key phrase extraction [ 4 ], hyperspectral image classification [ 5 ], multidimensional essay scoring [ 6 ], relation extraction [ 7 ], speech recognition [ 8 ], sentiment classification [ 9 ], geospatial market segmentation [ 10 ], fake news detection [ 11 ], question answering [ 12 ], text summarization [ 13 ], and text generation [ 14 ]. Good surveys of transformers and related attention technologies can be found in these references [ 45 , 46 ].…”

Section: Related Workmentioning

confidence: 99%

“…Transformers are currently state-of-the-art models in neural machine translation [1,2]. Since their introduction in 2017 [3], transformers have consistently produced state-of-the-art results in many areas of NLP and NMT [4][5][6][7][8][9][10][11][12][13][14]. However, based on recent results, it appears that transformers are reaching their limits.…”

Section: Introductionmentioning

confidence: 99%

Beyond the Transformer: A Novel Polynomial Inherent Attention (PIA) Model and Its Great Impact on Neural Machine Translation

ElAffendi

Alrajhi

2022

Computational Intelligence and Neuroscience

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This paper describes a novel polynomial inherent attention (PIA) model that outperforms all state-of-the-art transformer models on neural machine translation (NMT) by a wide margin. PIA is based on the simple idea that natural language sentences can be transformed into a special type of binary attention context vectors that accurately capture the semantic context and the relative dependencies between words in a sentence. The transformation is performed using a simple power-of-two polynomial transformation that maintains strict consistent positioning of words in the resulting vectors. It is shown how this transformation reduces the neural machine translation process to a simple neural polynomial regression model that provides excellent solutions to the alignment and positioning problems haunting transformer models. The test BELU scores obtained on the WMT-2014 data set are 75.07 BELU for the EN-FR data set and 66.35 BELU for the EN-DE data set—well above accuracies achieved by state-of-the-art transformer models for the same data sets. The improvements are, respectively, 65.7% and 87.42%.

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Seize market opportunities: market segmentation, profile and monitoring through user-generated content

Xiao-yan

Lin

Wang

et al. 2023

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PurposeThe purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).Design/methodology/approachThe specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.FindingsThe authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.Practical implicationsThe proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.Originality/valueThis study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

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Geo-Spatial Market Segmentation & Characterization Exploiting User Generated Text Through Transformers & Density-Based Clustering

Cited by 8 publications

References 40 publications

A Comprehensive Architecture for Federated Learning-Based Smart Advertising

A Comprehensive Architecture for Federated Learning-Based Smart Advertising

Beyond the Transformer: A Novel Polynomial Inherent Attention (PIA) Model and Its Great Impact on Neural Machine Translation

Seize market opportunities: market segmentation, profile and monitoring through user-generated content

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