首页|Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

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With the ever-increasing diversification of people's interests and preferences,artwork has become one of the most popular commodities or investment goods in E-commerce,and it increasingly attracts the attention of the public.Currently,many real-world or virtual artworks can be found in E-commerce,and finding a means to recommend them to appropriate users has become a significant task to alleviate the heavy burden on artwork selection decisions by users.Existing research mainly studies the problem of single-artwork recommendation while neglecting the more practical but more complex composite recommendation of artworks in E-commerce,which considerably influences the quality of experience of potential users,especially when they need to select a set of artworks instead of a single artwork.Inspired by this limitation,we put forward a novel composite recommendation approach to artworks by a user keyword-driven correlation graph search named ARTcom-rec.Through ARTcom-rec,the recommender system can output a set of artworks(e.g.,an artwork composite solution)in E-commerce by considering the keywords typed by a user to indicate his or her personalized preferences.Finally,we validate the feasibility of the ARTcom-rec approach by a set of simulated experiments on a real-world PW dataset.

composite recommendationartworkuser keywordsE-commercecorrelation graph search

Jingyun Zhang、Wenjie Zhu、Byoung Jin Ahn、Yongsheng Zhou

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School of Design,Dongseo University,Busan 47011,Republic of Korea

Institute of Art and Design,Jiangsu University of Technology,Changzhou 213001,China

Jinling Wenyun Art Design Co.,Ltd.,Zhenjiang 212000,China

Institute of Art and Design,Krirk University,Bangkok 10220,Thailand

Shandong Provincial University Laboratory for Protected Horticulture,Weifang University of Science and Technology,Shouguang 262700,China

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2024

10.26599/TST.2023.9010012

清华大学学报自然科学版(英文版)
清华大学

清华大学学报自然科学版(英文版)

CSTPCDEI
影响因子：0.474
ISSN：1007-0214
年,卷(期)：2024.29(1)
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