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


摘要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 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.
著者Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam
會議名稱The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'17)
會議地點Tokyo, Japan
會議論文集題名Proceedings of the The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'17)
頁次345 - 354
關鍵詞Rating Prediction, Tips Generation, Deep Learning

上次更新時間 2021-22-01 於 01:24