Which Strategy When? Designing an Adaptive Search System for Virtual Reality

Searching for information, objects, or places in virtual reality (VR) is often a cumbersome process that breaks user immersion. Existing interaction techniques like pointing or voice commands are context-dependent, yet current systems fail to help users select the optimal strategy for a given situation. To address this challenge, we introduce an adaptive VR search framework that recommends search strategies based on situational factors. We first propose a taxonomy of five common VR search strategies and construct a factorial dataset linking task types, object properties, and environmental conditions to strategy choices. We then conduct a two-stage study. In Stage 1, we capture users' natural strategy selections in a free-search environment to create a ground-truth dataset. In Stage 2, we use this data to train and evaluate two adaptive systems, one driven by a machine learning (ML) model and another by a Large Language Model (LLM), against the free-search baseline. Our results show that both adaptive systems significantly reduce the number of attempts and perceived workload compared to free search. While the ML-based system achieved the fastest task completion times, both adaptive approaches were rated as significantly more usable. This work demonstrates that adaptive, context-aware systems can enhance search efficiency and user experience in VR, paving the way for more intelligent and immersive information-seeking interfaces.