3VR: Vice Versa Virtual Reality Algorithm to Track and Map User Experience

Abstract

The understanding of how users interact with the virtual cultural heritage could provide digital curators valuable insights into user behaviors, and also improve the overall user experience through the ability to observe and record interactions of virtual visitors. This paper introduces the new User Behavior (UB) tracking algorithm that we developed investigating a salience of the Virtual Reality (VR) panoramic regions. The algorithm extracts the importance of Region of Interest (ROI) determining patterns of the visitors’ virtual movement and interest in combination with statistics of captured browser activity. The input of our algorithm is the virtual online interactive platform (Virtual Museum of the Civic Art Gallery of Ascoli Piceno in Italy) with eighty-one 16386x8192 pixels panoramic images and several interactive features including maps, thumbnails, and menus. The software engine of the tracking model “Vice Versa” VR (3VR) operates on inverse functions of all descriptive functions (descriptors), which are assigned particularly to each interactive feature such as viewing multimedia content and observing the panoramic environment. The tracking experiment was performed online and the web virtual museum key study collected behavior information from 171 visitors around the world. Collected data, multimedia and textual content, and the coordinates of the ROIs are then subjected to standard statistics operations in order to define common patterns of UBs. Thus, we have discovered that the ROIs are mostly mapped onto the artworks and it is possible to obtain patterns about the main interests of users. The developed tool offers a guideline for the panoramic tours design and the potential benefits for museums are to understand the public, verify the effectiveness of choices, and re-shape a cultural offer based on visitors’ needs. Exploiting this kind of user experience, our algorithm ensures relevant feedback during virtual visits, and thus paves the way for further development of the recommender system.