HH: Semantic web, although pretty much a failed project now, is pretty interesting as it imagined we could get machines to process data in a way that is decentralized and thus extend our knowledge. The decentralized nature of Tim Berners-Lee's vision of the semantic web is very admirable. That being said, giving every single piece of data an identifier in a reliable way is unworkable with original web technology. I am quite a fan of blockchain in this respect, as it offers some level of decentralization and cryptography and thus provides a better bedrock for social computing than the traditional web. I've mostly moved on from my research of semantic web, as it's only been used by large corporations for knowledge graphs and surveillance.

We recently asked Alexa if she could code a few medical charts for us. "Sorry I don't know that." After all, the U.S. healthcare industry spends billions of dollars on 250,000 medical coders every year to do the job. This way of doing business might be error-prone, inefficient, and bound by constantly changing regulations, but hey, IT IS a solution. But you know what else is a solution?

We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of "hiding in the crowd" privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or nym) setting and the BLC algorithm for carrying out this matrix factorization in a privacy-enhanced manner. We demonstrate that the increased privacy does not come at the cost of reduced recommendation accuracy.