# Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency

arXiv: https://arxiv.org/abs/2205.01759 · HTML: https://mlsanity.com/paper/2205.01759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 69 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://mlsanity.com/paper/1903.05987/) (2019)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [Neural Rating Regression with Abstractive Tips Generation for Recommendation](https://mlsanity.com/paper/1708.00154/) (2017)
- [Word Embeddings: A Survey](https://mlsanity.com/paper/1901.09069/) (2019)
- [Counterfactual Explanations for Neural Recommenders](https://mlsanity.com/paper/2105.05008/) (2021)
- [TransNets: Learning to Transform for Recommendation](https://mlsanity.com/paper/1704.02298/) (2017)
- [EXTRA: Explanation Ranking Datasets for Explainable Recommendation](https://mlsanity.com/paper/2102.10315/) (2021)
- [Enhancing Sentence Embedding with Generalized Pooling](https://mlsanity.com/paper/1806.09828/) (2018)
- [Sentence Embeddings in NLI with Iterative Refinement Encoders](https://mlsanity.com/paper/1808.08762/) (2018)
- [Visually Explainable Recommendation](https://mlsanity.com/paper/1801.10288/) (2018)

## Abstract (arXiv)

There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects interactions. Explaining why these relationships are established is essential to obtain transparency, an increasingly important notion. These explanations are often presented using text, thanks to the spread of the natural language understanding tasks. Our aim is to represent and explain pairs established by any agent (e.g., a recommender system or a paid promotion mechanism), so that text-based personalisation is taken into account. We have focused on the TripAdvisor platform, considering the applicability to other dyadic data contexts. The items are a subset of users and restaurants and the interactions the reviews posted by these users. We propose the PTER (Personalised TExt-based Reviews) model. We predict, from the available reviews for a given restaurant, those that fit to the specific user interactions. PTER leverages the BERT (Bidirectional Encoders Representations from Transformers) transformer-encoder model. We customised a deep neural network following the feature-based approach, presenting a LTR (Learning To Rank) downstream task. We carried out several comparisons of our proposal with a random baseline and other models of the state of the art, following the EXTRA (EXplanaTion RAnking) benchmark. Our method outperforms other collaborative filtering proposals.