User-Inspired Posterior Network for Recommendation Reason Generation

• URL: http://arxiv.org/abs/2102.07919v1
• Date: Tue, 16 Feb 2021 02:08:52 GMT
• Title: User-Inspired Posterior Network for Recommendation Reason Generation
• Authors: Haolan Zhan, Hainan Zhang, Hongshen Chen, Lei Shen, Yanyan Lan, Zhuoye Ding, Dawei Yin
• Abstract summary: Recommendation reason generation plays a vital role in attracting customers' attention as well as improving user experience. We propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests. Experimental results show that our model is superior to traditional generative models.
• Score: 53.035224183349385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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