Personalval

Personalval is a conceptual construct used in human-computer interaction and recommender systems to denote an individual's subjective value function over items, actions, or outcomes within a given domain. It captures the personal utility that a user associates with different options, and is thought to vary with context, mood, and goals. The term combines personal, emphasizing the user-specific nature, with value, reflecting the utility perspective in decision making.

Deriving a personalval involves collecting both explicit feedback (ratings, likes, disfavored items) and implicit signals (clicks, dwell time, purchase history). It is typically represented as a value function or preference model that maps options to utility scores. Methods include preference learning, collaborative filtering with user-specific priors, and inverse reinforcement learning to infer the latent reward structure guiding behavior. Personalval models may be static or adapt over time as the user interacts with systems.

Applications include personalized recommendations, tailored search results, adaptive user interfaces, and decision-support tools. By aligning system outputs with an individual’s estimated value function, these systems aim to improve relevance, satisfaction, and efficiency. Cross-domain use is an area of ongoing research, with challenges related to nonstationarity and context change.

Privacy and ethics are central concerns, as personalval synthesis relies on sensitive behavioral data. Best practices emphasize informed consent, data minimization, transparent use of collected information, and options for users to inspect or adjust their value models. Bias, discrimination, and feedback loops are potential risks.

Evaluation of personalval-based systems typically involves user-centric metrics, such as satisfaction, task success, and perceived usefulness, alongside standard offline measures for recommendation accuracy. Limitations include cold-start, sparsity, and the difficulty of disentangling value from other factors influencing behavior. See also: user model, preference elicitation, utility theory, recommender systems, personalization.