# UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation

arXiv: https://arxiv.org/abs/2209.13885 · HTML: https://mlsanity.com/paper/2209.13885/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 46 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Levenshtein Transformer](https://mlsanity.com/paper/1905.11006/) (2019)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [Insertion Transformer: Flexible Sequence Generation via Insertion Operations](https://mlsanity.com/paper/1902.03249/) (2019)
- [From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews](https://mlsanity.com/paper/1303.4402/) (2013)
- [CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling](https://mlsanity.com/paper/1811.10996/) (2018)
- [Personalized Prompt Learning for Explainable Recommendation](https://mlsanity.com/paper/2202.07371/) (2022)
- [Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation](https://mlsanity.com/paper/1904.03396/) (2019)
- [Personalized Transformer for Explainable Recommendation](https://mlsanity.com/paper/2105.11601/) (2021)
- [Non-Monotonic Sequential Text Generation](https://mlsanity.com/paper/1902.02192/) (2019)
- [Neural Rating Regression with Abstractive Tips Generation for Recommendation](https://mlsanity.com/paper/1708.00154/) (2017)
- [Insertion-based Decoding with automatically Inferred Generation Order](https://mlsanity.com/paper/1902.01370/) (2019)
- [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://mlsanity.com/paper/2202.13469/) (2022)
- [Personalized Showcases: Generating Multi-Modal Explanations for Recommendations](https://mlsanity.com/paper/2207.00422/) (2022)
- [Parallel Refinements for Lexically Constrained Text Generation with BART](https://mlsanity.com/paper/2109.12487/) (2021)
- [Sentence-Level Content Planning and Style Specification for Neural Text Generation](https://mlsanity.com/paper/1909.00734/) (2019)
- [KERMIT: Generative Insertion-Based Modeling for Sequences](https://mlsanity.com/paper/1906.01604/) (2019)
- [Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network](https://mlsanity.com/paper/2010.01480/) (2020)
- [Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding](https://mlsanity.com/paper/1906.05667/) (2019)
- [Knowledge-based Review Generation by Coherence Enhanced Text Planning](https://mlsanity.com/paper/2105.03815/) (2021)
- [Context-aware Natural Language Generation with Recurrent Neural Networks](https://mlsanity.com/paper/1611.09900/) (2016)

## Built on by (most cited first, 6 of 6)

- [Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era](https://mlsanity.com/paper/2403.08946/) (2024)
- [Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation](https://mlsanity.com/paper/2312.11554/) (2023)
- [MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation](https://mlsanity.com/paper/2408.09865/) (2024)
- [Explainable Recommendation with Simulated Human Feedback](https://mlsanity.com/paper/2504.14147/) (2025)
- [SumRec: A Framework for Recommendation using Open-Domain Dialogue](https://mlsanity.com/paper/2402.04523/) (2024)
- [Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation](https://mlsanity.com/paper/2504.11658/) (2025)

## Abstract (arXiv)

Personalized natural language generation for explainable recommendations plays a key role in justifying why a recommendation might match a user's interests. Existing models usually control the generation process by aspect planning. While promising, these aspect-planning methods struggle to generate specific information correctly, which prevents generated explanations from being convincing. In this paper, we claim that introducing lexical constraints can alleviate the above issues. We propose a model, UCEpic, that generates high-quality personalized explanations for recommendation results by unifying aspect planning and lexical constraints in an insertion-based generation manner. Methodologically, to ensure text generation quality and robustness to various lexical constraints, we pre-train a non-personalized text generator via our proposed robust insertion process. Then, to obtain personalized explanations under this framework of insertion-based generation, we design a method of incorporating aspect planning and personalized references into the insertion process. Hence, UCEpic unifies aspect planning and lexical constraints into one framework and generates explanations for recommendations under different settings. Compared to previous recommendation explanation generators controlled by only aspects, UCEpic incorporates specific information from keyphrases and then largely improves the diversity and informativeness of generated explanations for recommendations on datasets such as RateBeer and Yelp.