Neural Rating Regression with Abstractive Tips Generation for Recommendation
Refereed conference paper presented and published in conference proceedings


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AbstractRecently, 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 extbf{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.
All Author(s) ListPiji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam
Name of ConferenceThe 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'17)
Start Date of Conference07/08/2017
End Date of Conference11/08/2017
Place of ConferenceTokyo, Japan
Country/Region of ConferenceJapan
Proceedings TitleProceedings of the The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'17)
Year2017
PublisherACM
Pages345 - 354
ISBN978-1-4503-5022-8
LanguagesEnglish-United States
KeywordsRating Prediction, Tips Generation, Deep Learning

Last updated on 2021-24-02 at 00:36