Presentation
Extended Reality Experiences Prediction using Collaborative Filtering
Contributor
Event Type
ACM SIGGRAPH Asia Thesis Fast Forward
Doctoral Consortium
TimeSunday, 17 November 20199:00 - 11:00
LocationPlaza Meeting Room P2
DescriptionExtended Reality applications and simulators are increasingly becoming popular in business, but are often limited by their predictability and moreover, they lack personalization. The author proposes the usage of recommendation systems in extended reality simulators to solve this problem, through a platform in which extended reality experiences are suggested to the user using an item-based collaborative filtering approach with a precision of 71%. KNN is used to find clusters of similar items based on item similarity and user’s ratings. This platform leads to a new generation of smart simulators and can be highly valuable for personalized professional training and entertainment.
