Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition (2026.findings-eacl)
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| Challenge: | Existing systems for conversational recommender systems (CRS) have strong results in movies, but games present distinct challenges . MATCHA framework provides specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking, and stronger safety. |
| Approach: | They propose a framework for conversational recommender systems that assigns specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking and risk control. |
| Outcome: | MATCHA outperforms baselines on real user request dataset, improves Hit@5 by 20%, reduces popularity bias by 24%, and achieves 97.9% adversarial defense. |
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| Challenge: | Existing evaluation protocols for large language models (LLMs) are inadequate for conversational recommender systems. |
| Approach: | They propose an evaluation approach based on LLMs that harnesses LLM-based user simulators to evaluate ChatGPT's performance. |
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RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)
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| Challenge: | Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability. |
| Approach: | They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on a benchmark dataset. |
Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action (2025.findings-emnlp)
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| Challenge: | Conversational Recommender Systems (CRSs) aim to engage users in dialogue to provide tailored recommendations. |
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HARPO: Hierarchical Agentic Reasoning for User-Aligned Conversational Recommendation (2026.acl-long)
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| Challenge: | Recent advances in large language models have significantly improved conversational recommender systems performance. |
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CRSLab: An Open-Source Toolkit for Building Conversational Recommender System (2021.acl-demo)
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Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen
| Challenge: | Existing studies on conversational recommender systems lack a unified and standardized implementation or comparison. |
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One Agent To Rule Them All: Towards Multi-agent Conversational AI (2022.findings-acl)
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Christopher Clarke, Joseph Peper, Karthik Krishnamurthy, Walter Talamonti, Kevin Leach, Walter Lasecki, Yiping Kang, Lingjia Tang, Jason Mars
| Challenge: | Increasing volume of conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks. |
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LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive zero-shot capabilities in conversational recommender systems (CRS). |
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A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. |
| Approach: | They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs . |
| Outcome: | The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities . |
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning (2026.findings-acl)
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Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, Xian Li, Jianshu Chen, Dakuo Wang, Meng Jiang
| Challenge: | Existing studies focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions. |
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ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents (2020.acl-demos)
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Chia-Yu Li, Daniel Ortega, Dirk Väth, Florian Lux, Lindsey Vanderlyn, Maximilian Schmidt, Michael Neumann, Moritz Völkel, Pavel Denisov, Sabrina Jenne, Zorica Kacarevic, Ngoc Thang Vu
| Challenge: | Existing toolkits for developing dialog systems are limited to core components and do not support multi-modal processing and social signals. |
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