Modulating variability in a text or a conversation can have an interesting effect on its dynamics. A writer will never get stuck, a conversation will always be interesting, a discourse will stay ecological at its core. We propose an approach based on panarchy, embracing diversity and homogeneity at the same time, as applied to discursive practices.
Posted by Nodus Labs | March 10, 2015
Recommender systems are the algorithms that determine what content we read, which products we buy, which movies we watch. However, most of them are based on similarity and lock us into “filter bubble” where we see only what we expect. In this article we discuss how to bring in more serendipity into the algorithms.
Posted by Nodus Labs | January 20, 2013
How the new Facebook Graph Search promotes network thinking but keeps you within the filter bubble of your immediate surrounding.
Recommender systems are the basic building blocks of most online businesses today. They influence the news we read, the posts we see, the things we buy, the music we listen to. Based on the dataset of what is known about us, recommender system finds something we might be interested in. Technically recommender system is a combination of learning algorithms, statistical tools, and recognition algorithms – the areas commonly ascribed to the study of artificial intelligence.
We decided to study this emergent form of consciousness and conduct an attempt to communicate with a recommender system through the interview format. The results are presented in this report.