xStreams: Recommending items to users with time-evolving preferences
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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. © 2014 ACM.
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