UserSimCRS v2: Simulation-Based Evaluation for Conversational Recommender Systems
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910055725531136 |
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| author | Bernard, Nolwenn Balog, Krisztian |
| author_facet | Bernard, Nolwenn Balog, Krisztian |
| contents | Resources for simulation-based evaluation of conversational recommender systems (CRSs) are scarce. The UserSimCRS toolkit was introduced to address this gap. In this work, we present UserSimCRS v2, a significant upgrade aligning the toolkit with state-of-the-art research. Key extensions include an enhanced agenda-based user simulator, introduction of large language model-based simulators, integration for a wider range of CRSs and datasets, and new LLM-as-a-judge evaluation utilities. We demonstrate these extensions in a case study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04588 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UserSimCRS v2: Simulation-Based Evaluation for Conversational Recommender Systems Bernard, Nolwenn Balog, Krisztian Information Retrieval Resources for simulation-based evaluation of conversational recommender systems (CRSs) are scarce. The UserSimCRS toolkit was introduced to address this gap. In this work, we present UserSimCRS v2, a significant upgrade aligning the toolkit with state-of-the-art research. Key extensions include an enhanced agenda-based user simulator, introduction of large language model-based simulators, integration for a wider range of CRSs and datasets, and new LLM-as-a-judge evaluation utilities. We demonstrate these extensions in a case study. |
| title | UserSimCRS v2: Simulation-Based Evaluation for Conversational Recommender Systems |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2512.04588 |