![]() ![]() Over 500 experiments have been done to tweak our algorithms so they can deliver the best possible recommendations. With the (quite literally) massive base data set in place, the team then tested over 50 different recommender algorithms against a “gold standard” (which was itself revised five times for the best possible accuracy). These data sets often contain tens of millions of records each, and represent different dimensions which can all be applied to the problem of understanding what a user is looking for, and providing them with a high-quality set of recommendations. Mendeley’s Data Science team have been working to crack one of the hardest “big data” problems of all: How to recommend interesting articles that users might want to read? For the past six months they have been working to integrate 6 large data sets from 3 different platforms to create the basis for a recommender system. They’re here! Your new research features are now visible on – check it out now! ![]()
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