Title: Neural Rating Regression with Abstractive Tips Generation for Recommendation,
Abstract: Recently, some E-commerce sites launch a new interaction box called Tips on
their mobile apps. Users can express their experience and feelings or provide
suggestions using short texts typically several words or one sentence. In
essence, writing some tips and giving a numerical rating are two facets of a
user's product assessment action, expressing the user experience and feelings.
Jointly modeling these two facets is helpful for designing a better
recommendation system. While some existing models integrate text information
such as item specifications or user reviews into user and item latent factors
for improving the rating prediction, no existing works consider tips for
improving recommendation quality. We propose a deep learning based framework
named NRT which can simultaneously predict precise ratings and generate
abstractive tips with good linguistic quality simulating user experience and
feelings. For abstractive tips generation, gated recurrent neural networks are
employed to "translate" user and item latent representations into a concise
sentence. Extensive experiments on benchmark datasets from different domains
show that NRT achieves significant improvements over the state-of-the-art
methods. Moreover, the generated tips can vividly predict the user experience
and feelings.