Advances and Challenges in Conversational Recommender Systems: A Survey

By Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-seng Chua. AI Open, 2021

367 citations recorded by the source corpus.

Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to answer two important questions well due to inherent shortcomings: (a) What exactly does a user like? (b) Why does a user like an item? The shortcomings are due to the way that static models learn user preference, i.e., without explicit instructions and active feedback from users. The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally. In a CRS, users and the system can dynamically communicate through natural language interactions, which provide unprecedented opportunities to explicitly obtain the exact preference of users. Considerable efforts, spread across disparate settings and applications, have been put into developing CRSs. Existing models, technologies, and evaluation methods for CRSs are far from mature. In this paper, we provide a systematic review of the techniques used in current CRSs. We summarize the key challenges of developing CRSs in five directions: (1) Question-based user preference elicitation. (2) Multi-turn conversational recommendation strategies. (3) Dialogue understanding and generation. (4) Exploitation-exploration trade-offs. (5) Evaluation and user simulation. These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI). Based on these research directions, we discuss some future challenges and opportunities. We provide a road map for researchers from multiple communities to get started in this area. We hope this survey can help to identify and address challenges in CRSs and inspire future research.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/dde29ece7157b878bef72d59c473a2aa126c9b89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Wayne Xin Zhao
4. Tat-seng Chua
5. Yupeng Hou
6. Ji-rong Wen
7. Zhenguang Liu
8. Kun Zhou

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Wayne Xin Zhao
5. Ji-rong Wen
6. Yupeng Hou
7. Zhenguang Liu
8. Kun Zhou

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Wayne Xin Zhao
5. Yupeng Hou
6. Ji-rong Wen
7. Zhenguang Liu
8. Kun Zhou

Citations in the corpus

• CRSLab: An Open-Source Toolkit for Building Conversational Recommender System (2021, background): “, 2019), conversational recommender systems (CRSs) (Christakopoulou et al., 2016; Sun and Zhang, 2018; Gao et al., 2021) is”
• Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, positive): “To enhance the readability, we make some modifications to the original instructions that are used in our experiments.”
• Learning Intents behind Interactions with Knowledge Graph for Recommendation (2021, methodology): “These RL-based policy networks can be viewed as efficient and cheap alternatives to the brute-force search, which serve as the backbone models of conversational recommender systems [10, 21].”