# EGCR: Explanation Generation for Conversational Recommendation

arXiv: https://arxiv.org/abs/2208.08035 · HTML: https://mlsanity.com/paper/2208.08035/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018)
- [Towards Knowledge-Based Recommender Dialog System](https://mlsanity.com/paper/1908.05391/) (2019)
- [RevCore: Review-augmented Conversational Recommendation](https://mlsanity.com/paper/2106.00957/) (2021)
- [Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue](https://mlsanity.com/paper/1909.03922/) (2019)
- [CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation](https://mlsanity.com/paper/2010.10333/) (2020)
- [Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent](https://mlsanity.com/paper/2007.07422/) (2020)
- [A Corpus of Controlled Opinionated and Knowledgeable Movie Discussions for Training Neural Conversation Models](https://mlsanity.com/paper/2003.13342/) (2020)

## Built on by (most cited first, 2 of 2)

- [Towards Explainable Conversational Recommender Systems](https://mlsanity.com/paper/2305.18363/) (2023)
- [Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems](https://mlsanity.com/paper/2310.17749/) (2023)

## Abstract (arXiv)

Growing attention has been paid in Conversational Recommendation System (CRS), which works as a conversation-based and recommendation task-oriented tool to provide items of interest and explore user preference. However, existing work in CRS fails to explicitly show the reasoning logic to users and the whole CRS still remains a black box. Therefore we propose a novel end-to-end framework named Explanation Generation for Conversational Recommendation (EGCR) based on generating explanations for conversational agents to explain why they make the action. EGCR incorporates user reviews to enhance the item representation and increase the informativeness of the whole conversation. To the best of our knowledge, this is the first framework for explainable conversational recommendation on real-world datasets. Moreover, we evaluate EGCR on one benchmark conversational recommendation datasets and achieve better performance on both recommendation accuracy and conversation quality than other state-of-the art models. Finally, extensive experiments demonstrate that generated explanations are not only having high quality and explainability, but also making CRS more trustworthy. We will make our code available to contribute to the CRS community