DS4RS: Community-Driven and Explainable Dataset Search Engine for Recommender System Research

TL;DR

This paper introduces DS4RS, a community-driven, explainable search engine that improves dataset discoverability for recommender system research through semantic search and user contributions.

Contribution

It presents a novel platform combining semantic search, explainability, and community participation to enhance dataset accessibility for recommender system research.

Findings

Supports semantic search across multiple dataset attributes

Provides explanations of search relevance for transparency

Encourages community contribution of dataset metadata

Abstract

Accessing suitable datasets is critical for research and development in recommender systems. However, finding datasets that match specific recommendation task or domains remains a challenge due to scattered sources and inconsistent metadata. To address this gap, we propose a community-driven and explainable dataset search engine tailored for recommender system research. Our system supports semantic search across multiple dataset attributes, such as dataset names, descriptions, and recommendation domain, and provides explanations of search relevance to enhance transparency. The system encourages community participation by allowing users to contribute standardized dataset metadata in public repository. By improving dataset discoverability and search interpretability, the system facilitates more efficient research reproduction. The platform is publicly available at: https://ds4rs.com.