Advances and Challenges in Conversational Recommender Systems: A Survey

• URL: http://arxiv.org/abs/2101.09459v5
• Date: Sun, 7 Feb 2021 03:58:16 GMT
• Title: Advances and Challenges in Conversational Recommender Systems: A Survey
• Authors: Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua
• Abstract summary: We provide a systematic review of the techniques used in current conversational recommender systems (CRSs) We summarize the key challenges of developing CRSs into five directions. These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI)
• Score: 133.93908165922804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 into 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 helps to identify and address challenges in CRSs and inspire future research.

Related papers

• Towards Empathetic Conversational Recommender Systems [77.53167131692]
  We propose an empathetic conversational recommender (ECR) framework. ECR contains two main modules: emotion-aware item recommendation and emotion-aligned response generation. Our experiments on the ReDial dataset validate the efficacy of our framework in enhancing recommendation accuracy and improving user satisfaction.
  arXiv  Detail & Related papers  (2024-08-30T15:43:07Z)
• A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems [54.78815548652424]
  Conversational recommender systems generate recommendations through an interactive process. Not all CRS approaches use human conversations as their source of interaction data. holistic CRS are trained using conversational data collected from real-world scenarios.
  arXiv  Detail & Related papers  (2023-09-14T12:55:23Z)
• Meta Policy Learning for Cold-Start Conversational Recommendation [71.13044166814186]
  We study CRS policy learning for cold-start users via meta reinforcement learning. To facilitate policy adaptation, we design three synergetic components.
  arXiv  Detail & Related papers  (2022-05-24T05:06:52Z)
• BARCOR: Towards A Unified Framework for Conversational Recommendation Systems [40.464281243375815]
  We propose a unified framework based on BART for conversational recommendation. We also design and collect a lightweight knowledge graph for CRS in the movie domain.
  arXiv  Detail & Related papers  (2022-03-27T09:42:16Z)
• Learning to Ask Appropriate Questions in Conversational Recommendation [49.31942688227828]
  We propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation. KBQG models a user's preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph. Finially, accurate recommendations can be generated in fewer conversational turns.
  arXiv  Detail & Related papers  (2021-05-11T03:58:10Z)
• Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems [54.06971074217952]
  Conversational Recommender System (CRS) learns and models user's preferences through interactive dialogue conversations. Deep learning approaches are applied to CRS and have produced fruitful results.
  arXiv  Detail & Related papers  (2020-04-28T02:20:42Z)
• A Survey on Conversational Recommender Systems [11.319431345375751]
  Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing.
  arXiv  Detail & Related papers  (2020-04-01T18:00:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.