Abstract: The disclosure relates to a method for generating personalized dialogue content, in which an implicit association between personalized characteristics and corresponding dialogue replies is extracted by collecting a set of personalized dialogue data; a vector representation of a dialogue context and texts of the personalized characteristics is learned with a Transformer model; finally, through learning a sequence dependency between natural languages, a subsequent content may be automatically predicted and generated from a previous text, so that the generating of corresponding reply content may be achieved according to the dialogue context. With various optimization algorithms added, a generation probability of universal reply can be reduced and a diversity of the generated dialogue content can be improved.

Abstract: This disclosure provides a method for generating a personalized product description based on multi-source crowd data, which includes following steps: collecting data required for the personalized product description, the required data including reviews for crowd products and historical reviews of a crowd of users; portraiting the product and user to obtain a user preference label and a product label, which are then matched to obtain a personalized preference label; and generating the personalized product description in conjunction with the personalized preference labels. For different product attributes, different text generation methods are employed, and with different characteristics of the text generation methods such as extracted text generation and generated text generation, multi-source data are fused, so that the generated product description is smoother.