Online commodity recommendation model for interaction between user ratings and intensity-weighted hierarchical sentiment: A case study of LYCOM

The online commodity recommendation (OCR) model mines users’ historical behavior characteristics and recommends products that may be of interest according to user preferences. Online reviews are among the most important information sources for OCR. However, the explicit and implicit emotional words in online review texts have different structures in the expression of multi-attribute emotions. To fully utilize review information and improve the recommendation accuracy, we propose an OCR model that considers the interaction of multiple attributes and hierarchical emotions and calculates a score weighted by emotion intensity. First, to balance the efficiency and accuracy of information extraction while considering the coexistence of explicit and implicit expressions in online review text, a multi-attribute hierarchical emotion lexicon construction method is proposed. Second, based on the advantage of intuitionistic fuzzy sets in terms of information expression superiority, multi-attribute review text information expression of the affective polarity and intensity of online review text is realized. Then, combined with the weighted singular value decomposition and factorization machine method, we propose an OCR model for interactions between multi-attribute emotions and scores through fusion and recombination of the eigenvectors of users and products. Finally, tourism products on the LYCOM website are used as an example to verify the effectiveness of the proposed method.