Dataset-Agnostic Recommender Systems

2025-01-13

AI Summary

Current recommender systems exhibit poor cross-dataset generalization, rely heavily on manual hyperparameter tuning and feature engineering, and suffer from low reusability and deployment efficiency. To address these limitations, we propose the first dataset-agnostic recommender system paradigm, built upon a unified codebase that enables fully automated adaptation to arbitrary recommendation tasks. Our core innovation is DsDL (Dataset Description Language), a declarative metadata language that standardizes the structural and semantic specifications of datasets. Integrated with a metadata-driven architecture and automated machine learning orchestration, our framework autonomously performs end-to-end feature selection, missing-value imputation, noise filtering, and hyperparameter optimization. This approach significantly enhances the generality, reproducibility, and usability of recommender systems, substantially lowering the barrier to entry for non-expert users, and establishes a standardized infrastructure for industrial-scale deployment of recommendation technologies.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Feature Construction/Reformulation
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

[This is a position paper and does not contain any empirical or theoretical results] 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.

Problem

• Recommendation Systems
• Adaptability
• Parameter Optimization

Innovation

• DAReS
• DsDL
• Automated Parameter Optimization