CrowdDepict: Personalized Recommendation Content Generation Based on Heterogeneous Crowdsourced Data

Abstract

With the rapid development of the E-commerce, the text of the products?? advertisement and recommendation is extremely vital in the case that people cannot touch the real products. Traditional product descriptions which are written manually show the same product information to all users, which do not consider that different users pay attention to different attributes. Moreover, the efficiency of manually written product descriptions cannot match the growth rate of products, so how to automatically generate personalized product descriptions has become a frontier research problem. This paper mainly studies the generation of personalized product description, takes the personalized characteristics of users into account and generates the product description text corresponding to the interest for each user. Due to the lack of personalized product description data set, the CrowdDepict model is proposed. It collects relevant corpus through public data sources such as Douban and JD, and generates personalized product description with product comments, etc. The experimental results show that the personalized product description model CrowdDepict can automatically generate the personalized product description according to the user preference, the description covers the user interest and product characteristics, and the text expression is smooth.

Keywords

• deep learning
• recommendation content generation
• personalization
• natural language generation
• crowdsourced data