Metaverse User Portrait Construction Based on Human Gaze-tracking Data

With the rapid growth of virtual reality technology in the Metaverse, where a digital virtual world is integrated with the real world, users are exposed to a variety of content in different scenarios. Similar to the Web2 scenario, accurately profiling users in Metaverse applications is critical for improving user satisfaction. This involves collecting and inferring information such as users’ basic attributes, interests, and consumption habits. In this study, we propose a new approach for profiling users in virtual worlds based on gaze-tracking. We developed a Metaverse shopping experiment platform and recruited volunteers to record their attention time on different commodities in 3-D scenes by wearing eye-tracking devices. We treat this gaze-tracking data as implicit feedback to capture users’ interests, thereby predicting users’ attributes. We tried different ways of extracting features from collected gaze-tracking data. To our knowledge, we are the first to mine user portraits using gaze-tracking sequences. Our experimental results demonstrate that the fusion of sequence and image features significantly improves the accuracy of user gender prediction, reaching 88.89%, which is better than using sequence or image features only. These findings demonstrated the practical and effective advantages of using gaze-tracking data to build user portraits in the Metaverse.