Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products

Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 February 2025 V1 Latest version Share on Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products Authors : Haiying Ren 0000-0002-1197-6709 [email protected] , Jun Guan , and Jingru Guo Authors Info & Affiliations https://doi.org/10.22541/au.174012711.12854811/v1 Published Intelligent Systems in Accounting, Finance and Management Version of record Peer review timeline 177 views 67 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract To address the limitations of existing product concept design methods in the rapidly changing market environments, this study proposes a product concept design method using e-commerce product data and artificial intelligence techniques. First, data of competing e-commerce products are acquired from an e-commerce platform. Second, monthly sales of products are categorized and selected as the indicator for evaluating product concepts. Third, Doc2Vec is used to vectorize the product description to obtain the semantic representation of product concepts, and a machine learning–based product concept evaluation model is built using the concept vector as features. Finally, a product concept element library is built based on Word2Vec, and the tabu search algorithm is applied to identify the optimal combination of concept elements, determining the most favorable combination of product concepts for the new product. Results indicate that the product concept evaluation model based on multilayer perceptron achieves an average accuracy of 85.62% in predicting the quartiles of sales in the case of middle-aged and elderly home products, with the area under the receiver operating characteristic curve ranging from 0.96 to 0.99. The proposed product concept design method can produce novel product concepts with good market potential and a high degree of automation, improving the time efficiency and quality of product concept design. Supplementary Material File ((250221) intelligent product concept design method based on semantics of competing e-commerce products.docx) Download 710.92 KB Information & Authors Information Version history V1 Version 1 21 February 2025 Peer review timeline Published Intelligent Systems in Accounting, Finance and Management Version of Record 25 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords machine learning product concept design product concept evaluation text vectorization Authors Affiliations Haiying Ren 0000-0002-1197-6709 [email protected] Beijing University of Technology View all articles by this author Jun Guan Beijing University of Technology View all articles by this author Jingru Guo Beijing University of Technology View all articles by this author Metrics & Citations Metrics Article Usage 177 views 67 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Haiying Ren, Jun Guan, Jingru Guo. Intelligent Product Concept Design Method Based on Semantics of Competing E-Commerce Products. Authorea . 21 February 2025. DOI: https://doi.org/10.22541/au.174012711.12854811/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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