DorianTV is the product of over two years worth of research by Brian Dils focusing on the design of user-centered internet television systems. The research was used for Brian's Master's thesis in Human-computer Interaction Design at the School of Informatics, Indiana University. Although Brian has completed his degree, he continues to develop DorianTV. At the current time, DorianTV is only a prototype, but Brian hopes to have a live Beta up and running in the next few months.
DorianTV is an application that allows users to experience television through their internet browser. Unlike other video websites, such as Google Video or YouTube, DorianTV supports only video content that is considered professional television. This means the videos must be produced at a high quality level and have a regular release schedule.
DorianTV provides a “soft convergence” between television and the Internet. The system exploits the networked environment of the web, while replicating the ease of use and familiarity of traditional television. The strength of DorianTV lies in the discovery engine, which combining intelligent filtering, a tagging system, visualization tools, and a recommendation engine.
DorianTV features an embedded media player that allows for on-demand playback of content. All shows are stored on the DorianTV servers and can be accessed at any time. Users are not burdened with downloading shows to their local machine, nor do they have to subscribe to RSS feeds.
In order to insure professional quality video, DorianTV allows only video content with a bit rate of 500 kilobits/sec or higher. This is the level of quality that is considered the minimum number of bits per second to be considered professional grade.
Instead of keyword-based metadata, DorianTV uses a tagging system that allows users to “tag” the content. Not only does this allow for a metadata system based on user preferences, but popular tags can act as genre labels, effectively creating an emergent categorization scheme not seen in traditional television. Allowing users to create the metadata will also improve search results, as well as aid in content discovery through the recommendation engine.
Brian's research found that users of internet television needed improved ways of reaching content that interested them. DorianTV meets this need through a recommendation engine. Recommendations are made based on two relationships: similar viewership and shared tags. Because the system tracks the history of each user, the shows that are recommended are either watched by other viewers with similar viewing habits, or they share similar tags to shows that the viewer watches on a regular basis. In future iterations of DorianTV, a system will be designed to allow users to tweak the settings of recommendations.
Users also need a way to discover new content that is not closely related to they shows they regularly watch. For this reason, DorianTV employs a discovery engine in the form of a visualization tool [fig. 5]. This tool allows users to visualize relationships between shows in the DorianTV database. For every show on DorianTV, a visualization can be created showing how that show and other shows are related based on viewership.