Everyone who watches Netflix knows how easy it is to be physically unable to stop watching Netflix. This is partly because of the solid recommendations it provides, but also due to how awesome it is streaming movie after movie via Xbox on a Saturday night accompanied with Lay’s potato chips and Diet Coke. Personally, I also noticed the strange genres that Netflix would come up with to classify the movie I just watched and a potentially compatible movie that is one click away. Users, like myself, really do take for granted all the work and effort put into creating the metadata for the classification of all the movies simply so they can watch one just like it in a matter of seconds. I also want to know where I can get a job that requires you to watch movies all day.
This recommendation feature on Netflix is very convenient for users, which made me think of the idea of how it could be applied to different media or resources even. I was reminded of the program that is used in Scandinavian C171, a class I am taking about Scandinavian folk narrative. The professor actually spent many years writing a book (Danish Folktales, Legends, and Other Stories) that includes a CD that has access to a created digital database of thousands of mostly Danish folktales. This program uses metadata to classify the stories and each have a call number (e.g. DS_VII_505) that resememble those used in libraries. Metadata is also used for recommending other tales, much like how Netflix recommends, except without the goofy genre titles.
As seen in the screenshots of the program above, the stories’ pages provide as vast amount of information. Not only do the pages provide original manuscript transcription and translate, a map to show the origin, and dates of when it was told, but also sections dedicated to associated keywords (blue), story indices (green), and recommended stories (red). These recommended stories, much like Netflix recommendations, are for the user to continue reading without stopping, which is complete possibly because the recommended stories have different recommended stories which have different recommended stories etc. Since this use of metadata for recommendation, as seen on Netflix, also can be applied to Scandinavian folktales, there is no limit to how other media can also be grouped together and recommended at this time.
Example keywords: mound dweller, troll, ghosts, mares, coins, bottle, toad