Aspect-Based Fashion Recommendation With Attention Mechanism

With the rapid growth of fashion e-commerce, fashion recommendation has become a main digital marketing tool that is built on customer reviews and ratings. Online review is a powerful source for understanding users' shopping experiences, preferences and feedbacks on product/item performances, and thus is useful for enhancing personalized recommendations for future purchases. However, most extant fashion recommendation methods lack effective frameworks to integrate local and global aspect representations extracted from customers' ratings and reviews. In this paper, we proposed an aspect-based fashion recommendation model with attention mechanism (AFRAM) to predict customer ratings based on online reviews of fashion products. This model can extract latent aspect features about users and items separately through two parallel paths of convolutional neural networks (CNN), long short-term memory networks (LSTM), and attention mechanisms. One path processes user reviews and the other copes with item reviews. On each path, CNN and LSTM are both coupled with an attention mechanism to capture local aspect features and global aspect features respectively, which are combined through a mutual operation module. The mutual operations on both paths can enhance the generalization of the AFRAM model. The extracted features from the two paths are further merged to predict users' ratings. Real-world customer reviews and ratings collected from two renowned business websites were used to train and test AFRAM. The experiment results demonstrate that AFRAM is more effective in customer rating predictions, as compared to several state-of-the-art fashion recommenders.