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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 historical profiles. However, most of these works do not 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 "xStreams" first separates customers based on their historical data into clusters. It then employs collaborative filtering (CF) to recommend new items to the customers based on their group similarity. To evaluate the working of xStreams, we use a multi-relational data generator for streams. We evaluate xStreams on real and synthetic datasets.

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Abstract. Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning of models upon the primary objects (patients, customers) rather than the transactions, especially when these models are subjected to drift.We address this problem by combining advances of online clustering on multivariate data with the trajectory mining paradigm. We model the measurements of each individual primary object (e.g. its transactions), taken at irregular time intervals, as a trajectory in a high-dimensional feature space. Then, we cluster individuals with similar trajectories to identify sub-populations that evolve similarly, e.g. groups of customers that evolve similarly or groups of employees that have similar careers.We assume that the multivariate trajectories are generated by drifting Gaussian Mixture Models. We study (i) an EM-based approach that clusters these trajectories incrementally as a reference method that has access to all the data for learning, and propose (ii) an online algorithm based on a Kalman filter that efficiently tracks the trajectories of Gaussian clusters. We show that while both methods approximate the reference well, the algorithm based on a Kalman filter is faster by one order of magnitude compared to the EM-based approach.

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