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Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven correlation graph search

Tsinghua Science and Technology 2024年第1期29卷 174-184页

作者： Jingyun Zhang Wenjie Zhu Byoung Jin Ahn Yongsheng Zhou School of Design Dongseo UniversityBusan 47011Republic of Korea Institute of Art and Design Jiangsu University of TechnologyChangzhou 213001China Jinling Wenyun Art Design Co. Ltd.Zhenjiang 212000China Institute of Art and Design Krirk UniversityBangkok 10220Thailand Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang 262700China

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 ART_(com-rec).Through ART_(com-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 ART_(com-rec) approach by a set of simulated experiments on a real-world PW dataset.

关键词： composite recommendation artwork user keywords E-commerce correlation graph search

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