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    Adaptive virtual reality (VR) search systems recommend strategies to improve efficiency. Both machine learning (ML) and Large Language Model (LLM) systems reduced search attempts and workload, enhancing user experience.

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    Area of Science:

    • Human-Computer Interaction
    • Virtual Reality
    • Information Retrieval

    Background:

    • Virtual reality (VR) search is often inefficient, hindering user immersion.
    • Current VR systems lack adaptive strategies for optimal information seeking.

    Purpose of the Study:

    • To develop and evaluate an adaptive VR search framework.
    • To enhance search efficiency and user experience in virtual reality environments.

    Main Methods:

    • Proposed a taxonomy of five VR search strategies.
    • Collected user data on strategy selection in a free-search environment.
    • Trained and evaluated machine learning (ML) and Large Language Model (LLM) adaptive systems.

    Main Results:

    • Both adaptive systems significantly reduced search attempts and perceived workload compared to free search.
    • ML-based system achieved faster task completion; both adaptive systems improved usability.
    • Adaptive systems demonstrated enhanced search efficiency and user experience in VR.

    Conclusions:

    • Context-aware adaptive systems improve VR search efficiency and user experience.
    • This research paves the way for more intelligent and immersive VR interfaces.