TemPEST: Soft Template-based Personalized EDM Subject Generation Through Collaborative Summarization

Wen-Chih Peng, Yu-Hsiu Chen, Pin-Yu Chen, Hong-Han Shuai

Research output: Contribution to conferencePaper

Abstract

We address personalized Electronic Direct Mail (EDM) subject generation, which generates an attractive subject line for a product description according to user’s preference on different contents or writing styles. Generating personalized EDM subjects has a few notable differences from generating text summaries. The subject has to be not only faithful to the description itself but also attractive to increase the click-through rate. Moreover, different users may have different preferences over the styles of topics. We propose a novel personalized EDM subject generation model named Soft Template-based Personalized EDM Subject Generator (TemPEST) to consider the aforementioned users’ characteristics when generating subjects, which contains a soft template-based selective encoder network, a user rating encoder network, a summary decoder network and a rating decoder. Experimental results indicate that TemPEST is able to generate personalized topics and also effectively perform recommending rating reconstruction.
Original languageAmerican English
Number of pages8
DOIs
StatePublished - 2020
EventConference on Artificial Intelligence (AAAI), 2020 -
Duration: 1 Jan 2020 → …

Conference

ConferenceConference on Artificial Intelligence (AAAI), 2020
Period1/01/20 → …

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