# Deep Conversational Recommender in Travel

arXiv: https://arxiv.org/abs/1907.00710 · HTML: https://mlsanity.com/paper/1907.00710/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 16 of 16)

- [CRSLab: An Open-Source Toolkit for Building Conversational Recommender System](https://mlsanity.com/paper/2101.00939/) (2021)
- [C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System](https://mlsanity.com/paper/2201.02732/) (2022)
- [RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems](https://mlsanity.com/paper/2507.22897/) (2025)
- [Conversational Recommendation: A Grand AI Challenge](https://mlsanity.com/paper/2203.09126/) (2022)
- [HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management](https://mlsanity.com/paper/2105.06041/) (2021)
- [Multi-grained Hypergraph Interest Modeling for Conversational Recommendation](https://mlsanity.com/paper/2305.04798/) (2023)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [COLA: Improving Conversational Recommender Systems by Collaborative Augmentation](https://mlsanity.com/paper/2212.07767/) (2022)
- [Attention to Non-Adopters](https://mlsanity.com/paper/2510.15951/) (2025)
- [End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge Management](https://mlsanity.com/paper/2106.11796/) (2021)
- [A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems](https://mlsanity.com/paper/2309.07682/) (2023)
- [Category Aware Explainable Conversational Recommendation](https://mlsanity.com/paper/2103.08733/) (2021)
- [Improving Items and Contexts Understanding with Descriptive Graph for Conversational Recommendation](https://mlsanity.com/paper/2304.09093/) (2023)
- [Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts](https://mlsanity.com/paper/2504.13655/) (2025)
- [Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples](https://mlsanity.com/paper/2303.05575/) (2023)
- [Leveraging Knowledge Graph Embedding for Effective Conversational Recommendation](https://mlsanity.com/paper/2408.01342/) (2024)

## Abstract (arXiv)

When traveling to a foreign country, we are often in dire need of an intelligent conversational agent to provide instant and informative responses to our various queries. However, to build such a travel agent is non-trivial. First of all, travel naturally involves several sub-tasks such as hotel reservation, restaurant recommendation and taxi booking etc, which invokes the need for global topic control. Secondly, the agent should consider various constraints like price or distance given by the user to recommend an appropriate venue. In this paper, we present a Deep Conversational Recommender (DCR) and apply to travel. It augments the sequence-to-sequence (seq2seq) models with a neural latent topic component to better guide response generation and make the training easier. To consider the various constraints for venue recommendation, we leverage a graph convolutional network (GCN) based approach to capture the relationships between different venues and the match between venue and dialog context. For response generation, we combine the topic-based component with the idea of pointer networks, which allows us to effectively incorporate recommendation results. We perform extensive evaluation on a multi-turn task-oriented dialog dataset in travel domain and the results show that our method achieves superior performance as compared to a wide range of baselines.