text: Recommendation systems are recognised as being hugely important in industry, and the area is now well understood. At News UK, there is a requirement to be able to quickly generate recommendations for users on news items as they are published. However, little has been published about systems that can generate recommendations in response to changes in recommendable items and user behaviour in a very short space of time. In this paper we describe a new algorithm for updating collaborative filtering models incrementally, and demonstrate its effectiveness on clickstream data from The Times. We also describe the architecture that allows recommendations to be generated on the fly, and how we have made each component scalable. The system is currently being used in production at News UK.
  @cite_5: Over the last decade a vast number of businesses have developed online e-shops in the web. These online stores are supported by sophisticated systems that manage the products and record the activity of customers. There exist many research works that strive to answer the question "what items are the customers going to like" given their historic profiles. However, most of these works miss to take into account the time dimension and cannot respond efficiently when data are huge. In this paper, we study the problem of recommendations in the context of multi-relational stream mining. Our algorithm first separates customers based on their historic data into clusters. It then employs collaborative filtering (CF) to recommend new items to the customers based on their clustering structure. We evaluate our algorithm on two data sets, MovieLens and a synthetic data set.
summary: The xStreams system of @cite_5 does handle new users and items, however it does not incorporate the matrix factorization algorithms which provide the current state-of-the art recommendations.