In collaboration with Paolo Villanueva, Alya Alqarni and Yujie Jiang.
In October, Verizon approached Parsons Design + Technology students with three design questions,all of which revolved around the same premise:
How can Verizon leverage nontraditional methods to drive content discovery?
How can Verizon remove itself from the type-and-search formula?
After receiving the design charges, we thought of how one typically discovers content.
Typically, a user may come across new content via a sponsored/suggested post or a repost from a friend on social. Mindlessly scrolling past post after post, occasionally stumbling upon interesting and engaging content — but what if you could actually stumble upon something specific to your interests?
What if you could uncover something you didn’t realize was there?
With the app installed, users can be pinged when they stumble upon a “room” which they can enter to discover more content. These “rooms” can be anything from forgotten historical sites, augmented reality pop-up shops or branded content.
Users can “collect” these rooms to revisit again, anywhere, at any time.
Machine learning is used to study user behavior and interests and sift through content based on tags and meta data. From this, our AI builds procedurally generated rooms based on existing content and user behavior.
wondAR learns about your likes and interests based on the rooms you enter, how long you spend time in them and which ones you choose to “collect.” wondAR then generates and suggests new rooms for you, so you can immerse yourself in content that truly interests you.
Alya Alqarni, Yujie Jang, Paolo Villaneuva and Maddy Siriouthay.
