TemPEST: Soft Template-Based Personalized EDM Subject Generation through Collaborative Summarization


  • Yu-Hsiu Chen National Chiao Tung University
  • Pin-Yu Chen National Chiao Tung University
  • Hong-Han Shuai National Chiao Tung University
  • Wen-Chih Peng National Chiao Tung University




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.




How to Cite

Chen, Y.-H., Chen, P.-Y., Shuai, H.-H., & Peng, W.-C. (2020). TemPEST: Soft Template-Based Personalized EDM Subject Generation through Collaborative Summarization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 7538-7545. https://doi.org/10.1609/aaai.v34i05.6252



AAAI Technical Track: Natural Language Processing