Big & personal: data and models behind netflix recommendations

Since the Netflix $1 million Prize, announced in 2006, our company has been known to have personalization at the core of our product. Even at that point in time, the dataset that we released was considered "large", and we stirred innovation in the (Big) Data Mining research field. Our current product offering is now focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search. In this paper, we will discuss the different approaches we follow to deal with these large streams of data in order to extract information for personalizing our service. We will describe some of the machine learning models used, as well as the architectures that allow us to combine complex offline batch processes with real-time data streams.