Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915814957907968 |
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| author | Hui, Zheng Wei, Xiaokai Jiang, Yexi Gao, Kevin Wang, Chen Ong, Frank Yoon, Se-eun Pareek, Rachit Gong, Michelle |
| author_facet | Hui, Zheng Wei, Xiaokai Jiang, Yexi Gao, Kevin Wang, Chen Ong, Frank Yoon, Se-eun Pareek, Rachit Gong, Michelle |
| contents | Conversational recommender systems (CRS) have advanced with large language models, showing strong results in domains like movies. These domains typically involve fixed content and passive consumption, where user preferences can be matched by genre or theme. In contrast, games present distinct challenges: fast-evolving catalogs, interaction-driven preferences (e.g., skill level, mechanics, hardware), and increased risk of unsafe responses in open-ended conversation. We propose MATCHA, a multi-agent framework for CRS that assigns specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking with reflection, explanation, and risk control which enabling finer personalization, long-tail coverage, and stronger safety. Evaluated on real user request dataset, MATCHA outperforms six baselines across eight metrics, improving Hit@5 by 20%, reducing popularity bias by 24%, and achieving 97.9% adversarial defense. Human and virtual-judge evaluations confirm improved explanation quality and user alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20094 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition Hui, Zheng Wei, Xiaokai Jiang, Yexi Gao, Kevin Wang, Chen Ong, Frank Yoon, Se-eun Pareek, Rachit Gong, Michelle Information Retrieval Computation and Language Human-Computer Interaction Conversational recommender systems (CRS) have advanced with large language models, showing strong results in domains like movies. These domains typically involve fixed content and passive consumption, where user preferences can be matched by genre or theme. In contrast, games present distinct challenges: fast-evolving catalogs, interaction-driven preferences (e.g., skill level, mechanics, hardware), and increased risk of unsafe responses in open-ended conversation. We propose MATCHA, a multi-agent framework for CRS that assigns specialized agents for intent parsing, tool-augmented retrieval, multi-LLM ranking with reflection, explanation, and risk control which enabling finer personalization, long-tail coverage, and stronger safety. Evaluated on real user request dataset, MATCHA outperforms six baselines across eight metrics, improving Hit@5 by 20%, reducing popularity bias by 24%, and achieving 97.9% adversarial defense. Human and virtual-judge evaluations confirm improved explanation quality and user alignment. |
| title | Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition |
| topic | Information Retrieval Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.20094 |