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Qualifying Exam: Generating Explanation Sentences for Personalized Recommendation


Current explainable recommendation models mostly generate textual explanations based on pre-defined sentence templates. However, the expressiveness power of template-based explanation sentences is limited to the pre-defined expressions, and manually defining the expressions require significant human efforts. We propose a hierarchical sequence-to-sequence model for personalized explanation generation. Different from conventional sentence generation in NLP research, a great challenge of explanation generation in e-commerce recommendation is that not all sentences in user reviews are of explanation purpose. To solve the problem, we further propose an auto-denoising mechanism based on topical item feature words for sentence generation.

Hanxiong Chen
CoRE 305 (B)
Event Date: 
03/14/2019 - 3:00pm
Prof. Yongfeng Zhang (Chair), Prof. Matthew Stone, Prof. Gerard de Melo, Prof. Desheng Zhang
Event Type: 
Qualifying Exam
Dept. of Computer Science