Conversational Recommender Systems Using Generative Models (Gen-CRS): Literature Review

Description

This dataset contains a curated list of 49 research papers focused on Conversational Recommender Systems using Generative Models (Gen-CRS). The collection covers publications from 2018 to 2025 and reflects the rapid evolution of generative approaches in conversational recommendation scenarios.

The dataset was compiled in the context of the literature review “Conversational Recommender Systems Using Generative Models (Gen-CRS): A Literature Review” and the tutorial “A Tutorial on Recent Advances in Generative Conversational Recommender Systems”, presented at the ACM RecSys conference 2025. It serves as the bibliographic foundation for both contributions and is intended to support transparency, reproducibility, and further research in this area.

Each entry in the dataset corresponds to a single paper relevant to Gen-CRS, and the selection process, collection methodology, and inclusion criteria are provided in the accompanying literature review paper.

Dataset Structure

The dataset is organized in a tabular format, where each row corresponds to a single publication included in the literature collection. Rows contain the essential bibliographic metadata required to identify and retrieve the original paper.

The dataset includes the following columns:

• Paper title: Full title of the publication
• Author(s): Names of all authors as reported in the original paper
• Year: Year in which the paper was published
• Published at: Conference, workshop, journal, or other venue where the work appeared
• Reference Link/DOI: Persistent link to access the published document (e.g., DOI, publisher URL, or preprint reference)