Neural Rating Regression with Abstractive Tips Generation for Recommendation
Open accessAbstract
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.
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Early natural language generation work mainly relied on recurrent neural networks and often suffers from insufficient diversity due to limited model expressiveness.
“Early work [139-141] mainly relied on recurrent neural networks (e.g., LSTM [142], GRU [143]). Limited by the model's expressiveness, they often suffer from the issue of insufficient diversity.”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [139]
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Multi-class Review Rating Classification using Deep Recurrent Neural Network
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Self-citation
- Authors
- Li, P., Wang, Z., Ren, Z., Bing, L., & Lam, W.
- Affiliations
- Chinese University of Hong Kong (HK)
- Jingdong (China) (CN)
- Tencent (China) (CN)
- Venue
- Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2017-08-07
- Pages
- 345-354
- OpenAlex
- W2739992143
- Cited by
- 313
- References
- 58
- Citations by year
- 2026: 15 · 2025: 33 · 2024: 26 · 2023: 45 · 2022: 45 · 2021: 41 · 2020: 47 · 2019: 37 · 2018: 20 · 2017: 4
- FWCI
- 22.68
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Research Grant Council of the Hong Kong Special Administrative Region (14203414)
- Microsoft Research Asia Urban Informatics (FY14-RES-Sponsor-057)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- rating prediction, recommender systems, user experience, e-commerce, gated recurrent neural networks, online reviews
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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