@inproceedings{hui-etal-2026-toward,
title = "Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition",
author = "Hui, Zheng and
Wei, Xiaokai and
Jiang, Yexi and
Gao, Kevin and
Wang, Chen and
Yoon, Se-eun and
Pareek, Rachit and
Gong, Michelle",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.238/",
pages = "4568--4584",
ISBN = "979-8-89176-386-9",
abstract = "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. Code will be released upon acceptance."
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<abstract>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. Code will be released upon acceptance.</abstract>
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%0 Conference Proceedings
%T Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition
%A Hui, Zheng
%A Wei, Xiaokai
%A Jiang, Yexi
%A Gao, Kevin
%A Wang, Chen
%A Yoon, Se-eun
%A Pareek, Rachit
%A Gong, Michelle
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F hui-etal-2026-toward
%X 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. Code will be released upon acceptance.
%U https://aclanthology.org/2026.findings-eacl.238/
%P 4568-4584
Markdown (Informal)
[Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition](https://aclanthology.org/2026.findings-eacl.238/) (Hui et al., Findings 2026)
ACL
- Zheng Hui, Xiaokai Wei, Yexi Jiang, Kevin Gao, Chen Wang, Se-eun Yoon, Rachit Pareek, and Michelle Gong. 2026. Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition. In Findings of the Association for Computational Linguistics: EACL 2026, pages 4568–4584, Rabat, Morocco. Association for Computational Linguistics.