---
title: "Justifying Recommendations using Distantly-Labeled — Ni 2019"
description: "Explainable recommender systems often justify suggestions by generating review- or tip-like text."
authors: ["Jianmo Ni", "Jiacheng Li", "Julian McAuley"]
year: 2019
venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
source: "https://aclanthology.org/D19-1018/"
doi: "10.18653/v1/d19-1018"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ni-2019-justifying-recommendations-distantly-labeled-reviews/"
---
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# Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects

**Authors:** [Jianmo Ni](https://md.research.elamur.ai/free/en/authors/jianmo-ni/), Jiacheng Li, [Julian McAuley](https://md.research.elamur.ai/free/en/authors/julian-mcauley/) · **Year:** 2019 · **Venue:** Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D19-1018/) · [PDF](https://aclanthology.org/D19-1018.pdf) · [DOI 10.18653/v1/d19-1018](https://doi.org/10.18653/v1/d19-1018) · [OpenAlex](https://openalex.org/W2971196067)

**Citations:** 1200 citations (openalex-snapshot · 2026-10-14) · [221](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Explainable recommender systems often justify suggestions by generating review- or tip-like text. The authors argue that such text is a poor training target because much of it is generic praise or personal storytelling rather than concrete reasons for a choice. To get better training data, they split reviews into Elementary Discourse Units (EDUs) and filter them with simple rules. A small set is hand-labeled as good or bad justifications, and a fine-tuned BERT classifier then distantly labels whole Yelp and Amazon Clothing corpora. Fine-grained aspects are extracted from each justification to build user personas and item profiles.

On this data they train two generators. Ref2Seq, and its aspect-planning variant AP-Ref2Seq, attends to a user's and an item's past justifications. ACMLM, an aspect-conditional masked language model, fills in templates drawn from earlier justifications. Sampling-based models produce more diverse and informative output, while beam-search models score higher on BLEU. The work matters because it supplies large personalized justification datasets and shows that extracted justifications beat full reviews as a training source.

## Key points
- **Annotation:** two expert annotators labeled 1,000 + 600 EDU segments (Cohen's kappa 0.927 after alignment). About 24.8% of segments were judged good justifications.
- **Classification:** BERT gave the best F1 (~0.75) and precision (0.80) among the classifiers. A BERT sentiment model (BERT-SA) reached only ~0.49 F1, showing that positive sentiment is not the same as justification quality.
- **Transfer:** the classifier was trained only on Yelp labels but also worked on Amazon Clothing. Aspects were mined with the sentiment-lexicon method of Zhang et al. (2014).
- **Evaluation:** models were compared with BLEU and Distinct-1/2. Human raters scored 100 Yelp outputs per model on Relevance, Informativeness and Diversity (1–5 scale, at least 3 raters each).
- **Main finding:** Ref2Seq (Top-k) and ACMLM scored highest on human-rated Diversity and Informativeness. AP-Ref2Seq usually mentioned the aspects it was asked to plan, whereas models trained on whole reviews or tips produced generic phrases.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

221 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 212 not yet.

- [English Machine Reading Comprehension Datasets: A Survey](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/)
- [Benchmarking Arabic Authorship Attribution and Style Transfer with Large Language Models](https://md.research.elamur.ai/free/en/papers/hamed-2026-benchmarking-arabic-authorship-attribution-style/)
- [Bridging the Preference Gap between Retrievers and LLMs](https://md.research.elamur.ai/free/en/papers/ke-2024-bridging-preference-gap-between-retrievers/)
- [The Multilingual Amazon Reviews Corpus](https://md.research.elamur.ai/free/en/papers/keung-2020-multilingual-amazon-reviews-corpus/)
- [Exploiting Sentiment and Common Sense for Zero-shot Stance Detection](https://md.research.elamur.ai/free/en/papers/luo-2022-exploiting-sentiment-common-sense-zero/)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://md.research.elamur.ai/free/en/papers/nussbaum-2024-nomic-embed-training-reproducible-long/)
- [LaMP: When Large Language Models Meet Personalization](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://md.research.elamur.ai/free/en/papers/tseng-2024-two-tales-persona-llms-survey/)
- [Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ni2019justifying,
  title = {{Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects}},
  author = {Ni, Jianmo and Li, Jiacheng and McAuley, Julian},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1018},
  url = {https://doi.org/10.18653/v1/d19-1018}
}
```

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