This paper presents how to infer viewed exhibits in a metaverse museum from a visitor's movement log. This task consists of movement-state detection and viewed-exhibit inference. For the former task, we focus on visitor's fast and slow movement and discuss three differences between our previous methods and new methods: an additional parameter, speed normalization, and key-input accumulation. For the latter task, our new method focuses on the distance and angle between the visitor and each involving exhibit. According to a conducted experiment, the proposed methods could improve the performance (F-measure) by 10.3% and 4.6% for movement state detection and viewed-exhibit inference, respectively.
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