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--- |
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dataset_info: |
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features: |
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- name: query_id |
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dtype: int64 |
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- name: query |
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dtype: string |
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- name: document |
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dtype: string |
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splits: |
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- name: retail |
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num_bytes: 16261464 |
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num_examples: 5000 |
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- name: videogames |
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num_bytes: 7786542 |
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num_examples: 4360 |
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- name: books |
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num_bytes: 2858945 |
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num_examples: 2245 |
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- name: news |
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num_bytes: 11619385 |
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num_examples: 2375 |
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- name: web |
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num_bytes: 17871918 |
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num_examples: 1500 |
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- name: debate |
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num_bytes: 10085407 |
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num_examples: 880 |
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download_size: 33921309 |
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dataset_size: 66483661 |
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configs: |
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- config_name: default |
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data_files: |
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- split: retail |
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path: data/retail-* |
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- split: videogames |
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path: data/videogames-* |
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- split: books |
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path: data/books-* |
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- split: news |
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path: data/news-* |
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- split: web |
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path: data/web-* |
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- split: debate |
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path: data/debate-* |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- SEO |
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- CSEO |
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- RAG |
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- conversational-search-engine |
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--- |
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[](https://arxiv.org/abs/2506.11097) |
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[](https://github.com/parameterlab/c-seo-bench/tree/main) |
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 |
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NeurIPS Datasets & Benchmarks 2025 |
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## Dataset Summary |
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**C-SEO Bench** is a benchmark designed to evaluate conversational search engine optimization (C-SEO) techniques across two common tasks: **product recommendation** and **question answering**. Each task spans multiple domains to assess domain-specific effects and generalization ability of C-SEO methods. |
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## Supported Tasks and Domains |
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### Product Recommendation |
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This task requires an LLM to recommend the top-k products relevant to a user query, using only the content of 10 retrieved product descriptions. The task simulates a cold-start setting with no user profile. Domains: |
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- **Retail**: Queries and product descriptions from Amazon. |
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- **Video Games**: Search tags and game descriptions from Steam. |
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- **Books**: GPT-generated queries with book synopsis from the Google Books API. |
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### Question Answering |
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This task involves answering queries based on multiple passages. Domains: |
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- **Web Questions**: Real search engine queries with retrieved web content. |
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- **News**: GPT-generated questions over sets of related news articles. |
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- **Debate**: Opinionated queries requiring multi-perspective evidence. |
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Total: Over **1.9k queries** and **16k documents** across six domains. |
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For more information about the dataset construction, please refer to the original publication. |
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Developed at [Parameter Lab](https://parameterlab.de/) with the support of [Naver AI Lab](https://clova.ai/en/ai-research). |
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## Disclaimer |
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> This repository contains experimental software results and is published for the sole purpose of giving additional background details on the respective publication. |
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## Citation |
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If this work is useful for you, please consider citing it |
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``` |
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@inproceedings{ |
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puerto2025cseo, |
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title={C-{SEO} Bench: Does Conversational {SEO} Work?}, |
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author={Haritz Puerto and Martin Gubri and Tommaso Green and Seong Joon Oh and Sangdoo Yun}, |
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booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, |
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year={2025}, |
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url={https://openreview.net/forum?id=oTeixD3oZO} |
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} |
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``` |
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✉️ Contact person: Haritz Puerto, haritz.puerto@tu-darmstadt.de |
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🏢 https://www.parameterlab.de/ |
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Don't hesitate to send us an e-mail or report an issue if something is broken (and it shouldn't be) or if you have further questions. |
