Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces

2026-08-11

AI Summary

Traditional recommender systems simplify user behavior into static preferences, failing to capture complex intents such as exploration and comparison, thereby limiting their effectiveness in dynamic environments like generative user interfaces and extended reality. This work introduces inverse Theory of Mind (IToM) into recommender systems for the first time, inferring users’ underlying beliefs, preferences, and decision-making traits through counterfactual reasoning and multi-hypothesis abductive inference powered by large language models. The resulting structured, interpretable user profiles enable cross-modal transfer and intent-driven content presentation. Evaluated on the OPeRA dataset, the approach matches or surpasses performance using ground-truth user profiles across diverse tasks—including next-action prediction, shopping attitude alignment, Big Five personality inference, and category prediction—and has been successfully deployed in a VisionOS spatial banking application.

Abstract

Modern recommender systems treat observed actions as reliable proxies for user preferences, yet interactions often reflect exploration or comparison rather than stable preference expression. As interfaces evolve from static layouts toward generative UIs and immersive extended reality (XR), the need for deeper, modality-agnostic user understanding grows: these adaptive environments must decide not only what to present but where, when, how prominently, and most importantly why a user acts. We propose an Inverse Theory of Mind (IToM) pipeline that reasons backward from observed interactions to infer the beliefs, preferences, and decision-making traits that explain behavior. The pipeline reconstructs each user's decision context, including what was chosen and what alternatives were available, applies LLM-driven counterfactual reasoning to produce evidence-grounded natural-language belief statements, and synthesizes these beliefs through multi-hypothesis abductive inference into a structured user persona. We evaluate on the OPeRA dataset against ground-truth personality assessments, attitudinal surveys, and interview-based personas across four tasks: next action prediction, shopping attitude alignment, Big Five personality inference, and held-out category prediction. Results show that inferred personas match or exceed ground-truth personas and that multi-hypothesis reasoning is essential for accurate personality prediction. We further demonstrate cross-modal transferability with a persona-driven spatial banking application on VisionOS.

Problem

• recommender systems
• user behavior
• Theory of Mind
• adaptive interfaces
• preference inference

Innovation

• Inverse Theory of Mind
• counterfactual reasoning
• multi-hypothesis abductive inference
• structured user persona
• cross-modal transferability