Near-Realtime Webpage Recommendations “O...

Today, information overload is a problem pertinent to most information systems being used on a daily basis, with the World Wide Web chief among them. One of the key goals of Stumbleupon, a web content recommendation platform, is to ease this overload, while empowering discovery of relevant information. Our subscription to the “one recommendation at a time” concept focuses in producing an experience of serendipity as users continue to surf the web, while giving us the flexibility to reactively make recommendations near-realtime. In this presentation we will present the challenges that need to be addressed to extract content features from a web page and making near-realtime recommendations using them. We will describe the main algorithmic approach as well as the general architecture motivating our choices of tools, languages and platforms.