A Subjective and Behavioral Assessment of Affordances in Virtual Architectural Walkthroughs

Immersive technologies, such as VR, offer first-person experiences using depth perception and spatial awareness that elucidate a sense of space impossible with traditional visualization techniques. This paper looks beyond the visual aspects and towards understanding the experiential aspects of two popular uses of VR in 3D architectural visualization: a “passive walkthrough” and an “interactive walkthrough.” We designed a within-subject experiment to measure the user-perceived quality for both experiences. All participants (N = 34) were exposed to both scenarios and afterwards responded to a post-experience questionnaire; meanwhile, their physical activity and simple active behaviors were also recorded. Results indicate that while the fully immersive-interactive experience rendered a heightened sense of presence in users, overt behaviors (movement and gesture) did not change for users. We discuss the potential use of subjective assessments and user behavior analysis to understand user-perceived experiential quality inside virtual environments, which should be useful in building taxonomies and designing affordances that best fit these environments.

User Behavior Identification and Personalized Recommendation Based on Web Data Mining

A good understanding of user behavior and consumption preferences can provide support for website operators to improve their service quality. However, the existing personalized recommendation systems generally have problems such as low Web data mining efficiency, low degree of automated recommendation, and low durability. Targeting at these unsolved issues, this paper mainly carries out the following works: Firstly, the authors established a user behavior identification and personalized recommendation model based on Web data mining, it gave the user behavior analysis process based on Web data mining, improved the traditional k-means algorithm, and gave the detailed execution steps of the improved algorithm; moreover, it also elaborated on the K nearest neighbor model based on user scoring information, the score matrix decomposition method, and the personalized recommendation method for network users. At last, experimental results verified the effectiveness of the constructed model.