| | --- |
| | dataset_info: |
| | features: |
| | - name: query_id |
| | dtype: int64 |
| | - name: query |
| | dtype: string |
| | - name: document |
| | dtype: string |
| | splits: |
| | - name: retail |
| | num_bytes: 16261464 |
| | num_examples: 5000 |
| | - name: videogames |
| | num_bytes: 7786542 |
| | num_examples: 4360 |
| | - name: books |
| | num_bytes: 2858945 |
| | num_examples: 2245 |
| | - name: news |
| | num_bytes: 11619385 |
| | num_examples: 2375 |
| | - name: web |
| | num_bytes: 17871918 |
| | num_examples: 1500 |
| | - name: debate |
| | num_bytes: 10085407 |
| | num_examples: 880 |
| | download_size: 33921309 |
| | dataset_size: 66483661 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: retail |
| | path: data/retail-* |
| | - split: videogames |
| | path: data/videogames-* |
| | - split: books |
| | path: data/books-* |
| | - split: news |
| | path: data/news-* |
| | - split: web |
| | path: data/web-* |
| | - split: debate |
| | path: data/debate-* |
| | language: |
| | - en |
| | license: apache-2.0 |
| | tags: |
| | - SEO |
| | - CSEO |
| | - RAG |
| | - conversational-search-engine |
| | --- |
| | |
| | [](https://arxiv.org/abs/2506.11097) |
| | [](https://github.com/parameterlab/c-seo-bench/tree/main) |
| |
|
| |  |
| |
|
| | NeurIPS Datasets & Benchmarks 2025 |
| |
|
| | ## Dataset Summary |
| |
|
| | **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. |
| |
|
| | ## Supported Tasks and Domains |
| |
|
| | ### Product Recommendation |
| |
|
| | 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: |
| |
|
| | - **Retail**: Queries and product descriptions from Amazon. |
| | - **Video Games**: Search tags and game descriptions from Steam. |
| | - **Books**: GPT-generated queries with book synopsis from the Google Books API. |
| |
|
| | ### Question Answering |
| |
|
| | This task involves answering queries based on multiple passages. Domains: |
| |
|
| | - **Web Questions**: Real search engine queries with retrieved web content. |
| | - **News**: GPT-generated questions over sets of related news articles. |
| | - **Debate**: Opinionated queries requiring multi-perspective evidence. |
| |
|
| | Total: Over **1.9k queries** and **16k documents** across six domains. |
| |
|
| | For more information about the dataset construction, please refer to the original publication. |
| |
|
| | Developed at [Parameter Lab](https://parameterlab.de/) with the support of [Naver AI Lab](https://clova.ai/en/ai-research). |
| |
|
| |
|
| | ## Disclaimer |
| |
|
| | > This repository contains experimental software results and is published for the sole purpose of giving additional background details on the respective publication. |
| |
|
| |
|
| | ## Citation |
| | If this work is useful for you, please consider citing it |
| |
|
| | ``` |
| | @inproceedings{ |
| | puerto2025cseo, |
| | title={C-{SEO} Bench: Does Conversational {SEO} Work?}, |
| | author={Haritz Puerto and Martin Gubri and Tommaso Green and Seong Joon Oh and Sangdoo Yun}, |
| | booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, |
| | year={2025}, |
| | url={https://openreview.net/forum?id=oTeixD3oZO} |
| | } |
| | ``` |
| |
|
| | ✉️ Contact person: Haritz Puerto, haritz.puerto@tu-darmstadt.de |
| |
|
| | 🏢 https://www.parameterlab.de/ |
| |
|
| | 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. |
