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

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.