Paper Info
Title
DeepDepict: Enabling Information Rich, Personalized Product Description Generation With the Deep Multiple Pointer Generator Network
Abstract
AbstractIn e-commerce platforms, the online descriptive information of products shows significant impacts on the purchase behaviors. To attract potential buyers for product promotion, numerous workers are employed to write the impressive product descriptions. The hand-crafted product descriptions are less-efficient with great labor costs and huge time consumption. Meanwhile, the generated product descriptions do not take consideration into the customization and the diversity to meet users’ interests. To address these problems, we propose one generic framework, namely DeepDepict, to automatically generate the information-rich and personalized product descriptive information. Specifically, DeepDepict leverages the graph attention to retrieve the product-related knowledge from external knowledge base to enrich the diversity of products, constructs the personalized lexicon to capture the linguistic traits of individuals for the personalization of product descriptions, and utilizes multiple pointer-generator network to fuse heterogeneous data from multi-sources to generate informative and personalized product descriptions. We conduct intensive experiments on one public dataset. The experimental results show that DeepDepict outperforms existing solutions in terms of description diversity, BLEU, and personalized degree with significant margin gain, and is able to generate product descriptions with comprehensive knowledge and personalized linguistic traits.
Year
DOI
Venue
2021
10.1145/3446982
ACM Transactions on Knowledge Discovery from Data
Keywords
DocType
Volume
Personalized text generation, product description generation
Journal
15
Issue
ISSN
Citations 
5
1556-4681
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Shaoyang Hao100.34
Bin Guo29821.15
Hao Wang321656.92
Yunji Liang4918.61
Lina Yao54611.72
Qianru Wang652.14
Zhiwen Yu72753220.67