mlsanityData to 14 Oct 2026

Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systems

arXiv 2208.03516 ↗First posted 6 Aug 2022cs.CLcited by 8

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Abstract (arXiv)

Recommendation dialogue systems aim to build social bonds with users and provide high-quality recommendations. This paper pushes forward towards a promising paradigm called target-driven recommendation dialogue systems, which is highly desired yet under-explored. We focus on how to naturally lead users to accept the designated targets gradually through conversations. To this end, we propose a Target-driven Conversation Planning (TCP) framework to plan a sequence of dialogue actions and topics, driving the system to transit between different conversation stages proactively. We then apply our TCP with planned content to guide dialogue generation. Experimental results show that our conversation planning significantly improves the performance of target-driven recommendation dialogue systems.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 15 of its 21 references that are in the map. First posted on arXiv 6 Aug 2022.

Builds on

15 of 21 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
  6. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  7. 2020A Survey on Conversational Recommender Systemscited by 143
  8. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 113
  9. 2020Towards Conversational Recommendation over Multi-Type Dialogscited by 107
  10. 2020Towards Topic-Guided Conversational Recommender Systemcited by 94
  11. 2020INSPIRED: Toward Sociable Recommendation Dialog Systemscited by 93
  12. 2019Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialoguecited by 44
3 more
  1. 2020CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendationcited by 31
  2. 2021Learning Neural Templates for Recommender Dialogue Systemcited by 22
  3. 2019Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog Historycited by 20

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 2.

8 papers.

  1. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  2. 2023Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialoguecited by 14
  3. 2024ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agentscited by 12
  4. 2024The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trendscited by 11
  5. 2024Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialoguecited by 6
  6. 2025Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correctioncited by 2
  7. 2026Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluationcited by 2
  8. 2026SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selectioncited by 0