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Smarter search looks for influence rather than linksA Princeton search algorithm uses language indicators to measure importance.Researchers at Princeton University have been working to develop an algorithm to mine data by its importance, rather than by such indicators as links. The Princeton search algorithm parses language, identifying important words or phrases across a set of data, such as a group of documents or articles. It then measures the influence of those words across the data set to identify the most relevant information. It also looks at the way the language changes over time, which might make it applicable to real-time search applications. In Erica Naone's article for MIT's Technology Review, she discussed the research and highlighted possible applications for this type of search:
Such applications could be the answer to what journalist Mathew Ingram calls the "Holy Grail For News": recommendations. In a recent post, Ingram argued that none of the aggregation or news apps are achieving any impressive level of success in this area thus far. He suggested that social media, such as Facebook and Twitter, might provide the solution:
Princeton researchers might be able to add to that conversation, as well as to the future of search in general. Stephan Spencer, inventor and founder of Netconcepts, pointed to such possible search improvements in a 2009 post on the future of search:
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