# Neural Rating Regression with Abstractive Tips Generation for Recommendation

arXiv: https://arxiv.org/abs/1708.00154 · HTML: https://mlsanity.com/paper/1708.00154/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2017.

## Built on by (most cited first, 30 of 50)

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- [Personalized Transformer for Explainable Recommendation](https://mlsanity.com/paper/2105.11601/) (2021)
- [VIP5: Towards Multimodal Foundation Models for Recommendation](https://mlsanity.com/paper/2305.14302/) (2023)
- [XRec: Large Language Models for Explainable Recommendation](https://mlsanity.com/paper/2406.02377/) (2024)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Personalized Showcases: Generating Multi-Modal Explanations for Recommendations](https://mlsanity.com/paper/2207.00422/) (2022)
- [LLMRec: Benchmarking Large Language Models on Recommendation Task](https://mlsanity.com/paper/2308.12241/) (2023)
- [G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation](https://mlsanity.com/paper/2502.12586/) (2025)
- [Generate Natural Language Explanations for Recommendation](https://mlsanity.com/paper/2101.03392/) (2021)
- [Factual and Informative Review Generation for Explainable Recommendation](https://mlsanity.com/paper/2209.12613/) (2022)
- [Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation](https://mlsanity.com/paper/2305.14647/) (2023)
- [Uncertainty-Aware Explainable Recommendation with Large Language Models](https://mlsanity.com/paper/2402.03366/) (2024)
- [Explanation as a Defense of Recommendation](https://mlsanity.com/paper/2101.09656/) (2021)
- [Be Causal: De-biasing Social Network Confounding in Recommendation](https://mlsanity.com/paper/2105.07775/) (2021)
- [Comparative Explanations of Recommendations](https://mlsanity.com/paper/2111.00670/) (2021)
- [Debiased Explainable Pairwise Ranking from Implicit Feedback](https://mlsanity.com/paper/2107.14768/) (2021)
- [UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation](https://mlsanity.com/paper/2209.13885/) (2022)
- [COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation](https://mlsanity.com/paper/2210.15500/) (2022)
- [SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction](https://mlsanity.com/paper/2510.00080/) (2025)
- [Graph-based Extractive Explainer for Recommendations](https://mlsanity.com/paper/2202.09730/) (2022)
- [Towards Explainable Conversational Recommender Systems](https://mlsanity.com/paper/2305.18363/) (2023)
- [Knowledge-grounded Natural Language Recommendation Explanation](https://mlsanity.com/paper/2308.15813/) (2023)
- [MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation](https://mlsanity.com/paper/2408.09865/) (2024)
- [GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation](https://mlsanity.com/paper/2410.11841/) (2024)
- [Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation](https://mlsanity.com/paper/2410.13248/) (2024)
- [Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation](https://mlsanity.com/paper/2110.12648/) (2021)

## Abstract (arXiv)

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.