FFFN: Fashion Feature Fusion Network by Co-Attention Model for Fashion Recommendation

Fashion complementary recommendation has always been crucial in the field of recommendation. In previous work, researchers did not pay attention to the connection and combinability between multi-dimensional image features of fashion items. To effectively utilize the advantages of high-level and low-level features in images, we propose a Fashion Feature Fusion Network (FFFN) to solve the fashion complementary recommended tasks, which extracts and combines the features of different dimensions in the neural network into a fusion feature. Then, we input the fused image features and the category text features of fashion items into the co-attention module to realize the guidance of the text attention information to the image attention information. Experimental results on different datasets show that our model has advantages compared to the state-of-the-art methods.