CrowdDepict:多源群智数据驱动的个性化商品描述生成方法

CrowdDepict:Personalized Recommendation Content Generation Based on Heterogeneous Crowdsourced Data

随着网上购物逐渐发展,在无法接触到实体商品的情况下,商品描述显得尤为重要。传统人工撰写的商品描述语对所有用户展示相同的商品信息,但没有考虑到不同用户所关注的不同属性,并且人工撰写的效率无法与产品增长速度相匹配,因此如何自动生成个性化产品描述成为前沿研究问题。主要研究个性化商品描述内容生成,考虑用户的个性化特征,对每个用户生成对应其兴趣点的商品描述文本。因为个性化商品描述数据集的缺失,提出CrowdDepict方法,通过豆瓣、京东等公开数据源获取商品描述相关语料处理,利用商品评论等数据,生成商品个性化描述。实验结果表明,提出的个性化商品描述模型可根据用户偏好自动生成个性化的商品描述,内容覆盖用户兴趣与产品特点,文本表达流畅。

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.