Dataset-Agnostic Recommender Systems

TL;DR

This paper introduces Dataset-Agnostic Recommender Systems (DAReS), a framework that enables recommender systems to adapt to various datasets automatically using a structured dataset description language, reducing manual tuning and domain expertise.

Contribution

The paper presents DAReS, a novel approach that leverages the Dataset Description Language (DsDL) to automate dataset understanding and system configuration, enhancing reusability and scalability.

Findings

DAReS can adapt to different datasets without manual tuning.

The system automates feature selection, imputation, and hyperparameter optimization.

DAReS improves efficiency and accessibility for non-experts.

Abstract

Recommender systems have become a cornerstone of personalized user experiences, yet their development typically involves significant manual intervention, including dataset-specific feature engineering, hyperparameter tuning, and configuration. To this end, we introduce a novel paradigm: Dataset-Agnostic Recommender Systems (DAReS) that aims to enable a single codebase to autonomously adapt to various datasets without the need for fine-tuning, for a given recommender system task. Central to this approach is the Dataset Description Language (DsDL), a structured format that provides metadata about the dataset's features and labels, and allow the system to understand dataset's characteristics, allowing it to autonomously manage processes like feature selection, missing values imputation, noise removal, and hyperparameter optimization. By reducing the need for domain-specific expertise and manual adjustments, DAReS offers a more efficient and scalable solution for building recommender systems across diverse application domains. It addresses critical challenges in the field, such as reusability, reproducibility, and accessibility for non-expert users or entry-level researchers.