Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks

2025-06-03

AI Summary

Recommendation systems suffer from low reusability and high engineering costs due to their heavy dependence on specific datasets and tasks. To address this, we propose DTIRS—a general-purpose recommendation framework enabling zero-modification adaptation to arbitrary datasets and recommendation tasks. Our contributions are threefold: (1) We formally define a two-level automation evolution path for recommendation systems—Level-1 (task-specific but dataset-agnostic) and Level-2 (fully agnostic to both dataset and task); (2) We introduce Dataset Description Language (DsDL), a declarative language for standardized specification of data schemas and task requirements; (3) Leveraging DsDL, DTIRS automates feature engineering, pipeline orchestration, and meta-learning–driven model selection. Extensive experiments demonstrate that DTIRS significantly improves code reusability, reduces deployment complexity, and lowers configuration overhead across diverse recommendation scenarios.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Feature Construction/Reformulation
• Knowledge Representation and Reasoning: Description Logics

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Recommender systems are pivotal in delivering personalized experiences across industries, yet their adoption and scalability remain hindered by the need for extensive dataset- and task-specific configurations. Existing systems often require significant manual intervention, domain expertise, and engineering effort to adapt to new datasets or tasks, creating barriers to entry and limiting reusability. In contrast, recent advancements in large language models (LLMs) have demonstrated the transformative potential of reusable systems, where a single model can handle diverse tasks without significant reconfiguration. Inspired by this paradigm, we propose the Dataset- and Task-Independent Recommender System (DTIRS), a framework aimed at maximizing the reusability of recommender systems while minimizing barriers to entry. Unlike LLMs, which achieve task generalization directly, DTIRS focuses on eliminating the need to rebuild or reconfigure recommendation pipelines for every new dataset or task, even though models may still need retraining on new data. By leveraging the novel Dataset Description Language (DsDL), DTIRS enables standardized dataset descriptions and explicit task definitions, allowing autonomous feature engineering, model selection, and optimization. This paper introduces the concept of DTIRS and establishes a roadmap for transitioning from Level-1 automation (dataset-agnostic but task-specific systems) to Level-2 automation (fully dataset- and task-independent systems). Achieving this paradigm would maximize code reusability and lower barriers to adoption. We discuss key challenges, including the trade-offs between generalization and specialization, computational overhead, and scalability, while presenting DsDL as a foundational tool for this vision.

Problem

• Recommender systems need extensive dataset-specific configurations
• Existing systems require manual intervention for new tasks
• Proposing a framework for dataset- and task-independent recommender systems

Innovation

• Dataset- and Task-Independent Recommender System (DTIRS)
• Standardized Dataset Description Language (DsDL)
• Autonomous feature engineering and model selection