Product typicality attribute mining method based on a topic clustering ensemble

Sun, Jing-Tao; Zhang, Qiu-yu

doi:10.1007/s10462-022-10163-y

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…Product attributes extraction In the field of product design, topic extraction can be used to extract product attributes (e.g., the screen, battery, weight, and camera of a smartphone) and opinions [125]. As each product has multiple attributes, and consumers have varying preferences and evaluations for each attribute, extracting attributes from online reviews becomes essential [115].…”

Section: Data Processingmentioning

confidence: 99%

Big Data and AI-driven Product Design: A Survey

Quan

Zeng

et al. 2023

Preprint

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As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI driven product design methodology with significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review on big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited in aiding product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

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Section: Data Processingmentioning

confidence: 99%

Big Data and AI-driven Product Design: A Survey

Quan

Zeng

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Product attributes extraction. In the field of product design, topic extraction can be used to extract product attributes (e.g., the screen, battery, weight, and camera of a smartphone) and opinions [120]. As each product has multiple attributes, and consumers have varying preferences and evaluations for each attribute, extracting attributes from online reviews becomes essential [110].…”

Section: Product Design Based On Textual Datamentioning

confidence: 99%

“…The rapid development of NLP is the key to product design based on textual data, which including topic extraction, opinion mining, text classification, sentiment analysis, and text clustering.Product attributes extraction. In the field of product design, topic extraction can be used to extract product attributes (e.g., the screen, battery, weight, and camera of a smartphone) and opinions[120]. As each product has multiple attributes, and consumers have varying preferences and evaluations for each attribute, extracting attributes from online reviews becomes essential[110].…”

mentioning

confidence: 99%

Big Data and AI-Driven Product Design: A Survey

Quan,

Li,

Zeng

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

show abstract

Product typicality attribute mining method based on a topic clustering ensemble

Cited by 2 publications

References 32 publications

Big Data and AI-driven Product Design: A Survey

Big Data and AI-driven Product Design: A Survey

Big Data and AI-Driven Product Design: A Survey

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