Personalized Review Generation By Expanding Phrases And Attending On Aspect-Aware Representations

In this paper, we focus on the problem of building assistive systems that can help users to write reviews. We cast this problem using an encoder-decoder framework that generates personalized reviews by expanding short phrases (e.g. review summaries, product titles) provided as input to the system. We incorporate aspect-level information via an aspect encoder that learns 'aspect-aware' user and item representations. An attention fusion layer is applied to control generation by attending on the outputs of multiple encoders. Experimental results show that our model is capable of generating coherent and diverse reviews that expand the contents of input phrases. In addition, the learned aspectaware representations discover those aspects that users are more inclined to discuss and bias the generated text toward their personalized aspect preferences.