Neural Rating Regression with Abstractive Tips Generation for Recommendation

  • Journal/Conference Proceedings
    SIGIR’17, Tokyo, Japan
  • Publication Time
  • Authors
    Li, Piji, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam.
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.