Neural Rating Regression with Abstractive Tips Generation for Recommendation

TL;DR

This paper introduces NRT, a deep learning framework that jointly predicts ratings and generates human-like tips to better capture user experience and feelings, improving recommendation quality.

Contribution

The novel NRT model simultaneously predicts ratings and generates abstractive tips, integrating user and item representations for enhanced recommendation accuracy.

Findings

NRT outperforms state-of-the-art methods on benchmark datasets.

Generated tips effectively reflect user experience and feelings.

Joint modeling improves recommendation precision.

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.