Controllable Learning (CL) is being recognized as a vital element of reliable machine learning, one that ensures learning models meet set targets and can adapt to changing requirements without the need for retraining. This article examines the methods and applications of CL, focusing on its implementation within Information Retrieval (IR) systems, as demonstrated by researchers from Renmin University of China.
CL is technically defined as a learning system’s capacity to adjust to diverse task requirements without necessitating retraining. It ensures the learning model meets the user’s specific needs and targets, thereby increasing the system’s dependability and effectiveness. In IR applications, where the context and requirements often change, CL is particularly important for dealing with the dynamic and intricate nature of information needs.
The classification of CL is based on several factors: who controls the learning process, whether users or platforms; what aspects are controllable, such as retrieval objectives, user behavior, and environmental adaptation; the implementation of control, through methods like rule-based techniques, Pareto optimization, and Hypernetwork; and where control is applied, be it pre-processing, in-processing or post-processing.
User-centric control allows users to actively shape their recommendation experiences, modifying their profiles, and interactions to directly influence the output of recommendation systems. Methods like UCRS and LACE enable users to manage their profiles, interactions, and preferences to ensure recommendations reflect their changing needs and desires.
Meanwhile, platform-mediated control involves adjustments to algorithms and the imposition of policy-based constraints by the platform. Aims include enhancing the recommendation process by finding a balance between accuracy, diversity, and user satisfaction. Techniques like ComiRec and CMR use hypernetworks to dynamically generate parameters that adjust to shifting user preferences and environmental changes, ensuring a custom-tailored recommendation experience.
Numerous techniques are used to implement control in learning systems. Rule-based techniques apply predetermined rules to refine and enhance AI model output to ensure that it meets specific performance metrics. Pareto Optimization balances multiple conflicting objectives by finding optimal trade-offs, while hypernetworks generate parameters for another network, offering a flexible way to dynamically manage and adapt model parameters. The latter enhances the model’s adaptability and performance across various tasks and domains.
CL is particularly valuable in IR due to the complex and evolving nature of user information needs. The adaptability of CL techniques means learning models can dynamically adjust to different task descriptions, delivering personalized and relevant search results without extensive retraining.
In conclusion, a survey of CL underlines its crucial role in ensuring adaptable and reliable machine learning systems. Offering an overview of CL’s methods, applications, and challenges, it provides a helpful resource for those interested in the future of trustworthy machine learning and information retrieval.
