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. Figure 1 : Examples of reviews and tips selected from the restaurant "Gary Danko" on Yelp. Tips are more concise than reviews and can reveal user experience, feelings, and suggestions with only a few words. Users will get conclusions about this restaurant immediately after scanning the tips with their mobile phones.
NRT REF combines gated recurrent neural networks and collaborative ltering to simultaneously predict ratings and generate abstractive tips simulating user experience and feelings.
Neural Rating Regression with Abstractive Tips Generation for Recommendation
SIGIR '17
Computer Science