一套无需调参的推荐系统，自动适配不同数据集。
Dataset-Agnostic Recommender Systems
- 用结构化元数据描述数据特征，实现自动处理
- 支持特征选择、缺失值填补、噪声清除等全流程自动化
- 适合非专家或初学者快速构建可复现的推荐系统
推荐系统已成为个性化用户体验的核心，但其开发通常需要大量人工干预，包括针对特定数据集的特征工程、超参数调优和配置。为此，我们提出一种新范式：数据集无关推荐系统（DAReS），旨在通过单一代码库自主适应多种数据集，无需微调即可完成给定推荐任务。该方法的核心是数据描述语言（DsDL），一种结构化格式，用于提供数据集特征与标签的元信息，使系统能理解数据特性，进而自主完成特征选择、缺失值填补、噪声去除及超参数优化等流程。该方法减少了对领域专业知识和手动调整的需求，为跨应用场景构建推荐系统提供了更高效、可扩展的解决方案，解决了可复用性、可重现性和非专家用户可用性等关键挑战。
原文摘要 · Abstract (English)
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.
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