  Recommendation reason generation, aiming at showing the selling points of
products for customers, plays a vital role in attracting customers' attention
as well as improving user experience. A simple and effective way is to extract
keywords directly from the knowledge-base of products, i.e., attributes or
title, as the recommendation reason. However, generating recommendation reason
from product knowledge doesn't naturally respond to users' interests.
Fortunately, on some E-commerce websites, there exists more and more
user-generated content (user-content for short), i.e., product
question-answering (QA) discussions, which reflect user-cared aspects.
Therefore, in this paper, we consider generating the recommendation reason by
taking into account not only the product attributes but also the
customer-generated product QA discussions. In reality, adequate user-content is
only possible for the most popular commodities, whereas large sums of long-tail
products or new products cannot gather a sufficient number of user-content. To
tackle this problem, we propose a user-inspired multi-source posterior
transformer (MSPT), which induces the model reflecting the users' interests
with a posterior multiple QA discussions module, and generating recommendation
reasons containing the product attributes as well as the user-cared aspects.
Experimental results show that our model is superior to traditional generative
models. Additionally, the analysis also shows that our model can focus more on
the user-cared aspects than baselines.
