Complementary product recommendation aims to provide users with products that are often purchased together to meet common needs. Existing complementary product recommendation methods mainly consider modeling the content characteristics(visual and textual content) of products, but do not consider the preferences of users when purchasing products. This paper designed a complementary product recommendation model that integrated user preferences, termed as CPRUP. The model firstly computed the complementarity of image and text features between products; then it combined knowledge graph with attention mechanism, mined the correlation between user's historical purchased products based on n-hop neighbors, and proposed a user representation based on knowledge graph to extract user preferences for complementary products. Finally, it implemented joint learning of complementarity and user preferences based on neural networks. Experiments on the Amazon dataset show that the proposed CPRUP model has increased the ACC by 5% and the precision by 4% compared to the suboptimal baseline model, which indicates that the CPRUP model can more accurately recommend complementary products to users.

[2023-07-14] Accepted Paper /doc/2023.04.0164/v1

[2023-12-05] Printed Article /doc/2023.04.0164/v2