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We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-the-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Our formulation can also address cold-start problems by gracefully meshing collaborative and content-based reasoning. Finally, we present efficient Graphical Processing Unit (GPU) implementations and demonstrate a speedup of more than 270 times over our baseline CPU implementation on a cluster of 16 GPUs. [ABSTRACT FROM AUTHOR]

Protection of one’s intellectual property is a topic with important technological and legal facets. We provide mechanisms for establishing the ownership of a dataset consisting of multiple objects. The algorithms also preserve important properties of the dataset, which are important for mining operations, and so guarantee both right protection and utility preservation. We consider a right-protection scheme based on watermarking. Watermarking may distort the original distance graph. Our watermarking methodology preserves important distance relationships, such as: the Nearest Neighbors (NN) of each object and the Minimum Spanning Tree (MST) of the original dataset. This leads to preservation of any mining operation that depends on the ordering of distances between objects, such as NN-search and classification, as well as many visualization techniques. We prove fundamental lower and upper bounds on the distance between objects post-watermarking. In particular, we establish a restricted isometry property, i.e., tight bounds on the contraction/expansion of the original distances. We use this analysis to design fast algorithms for NN-preserving and MST-preserving watermarking that drastically prune the vast search space. We observe two orders of magnitude speedup over the exhaustive schemes, without any sacrifice in NN or MST preservation. [ABSTRACT FROM PUBLISHER]

The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractical. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the increasing deployment of mobile devices and real-time sensors has brought home the need for representations that can be incrementally updated and can approximate the data with fidelity proportional to its age. The latter property allows us to answer queries about the recent past with greater precision, since in many domains, recent information is more useful than older information. We call such representations amnesic. While there has been previous work on amnesic representations, the class of amnesic functions possible was dictated by the representation itself. In this work, we introduce a novel representation of time series that can represent arbitrary user-specified amnesic functions. For example, a meteorologist may decide that data that is twice as old can tolerate twice as much error and thus specify a linear amnesic function. In contrast, an econometrist might opt for an exponential amnesic function. We propose online algorithms for our representation and discuss their properties. Finally, we perform an extensive empirical evaluation on 40 data sets and show that our approach can efficiently maintain a high-quality amnesic approximation. [ABSTRACT FROM AUTHOR]

Smartphones are nowadays equipped with a number of sensors, such as WiFi, GPS, accelerometers, etc. This capability allows smartphone users to easily engage in crowdsourced computing services, which contribute to the solution of complex problems in a distributed manner. In this work, we leverage such a computing paradigm to solve efficiently the following problem: comparing a query trace Q against a crowd of traces generated and stored on distributed smartphones. Our proposed framework, coined SmartTrace+, provides an effective solution without disclosing any part of the crowd traces to the query processor. SmartTrace+, relies on an in-situ data storage model and intelligent top-K query processing algorithms that exploit distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q. We evaluate our algorithms on both synthetic and real workloads. We describe our prototype system developed on the Android OS. The solution is deployed over our own SmartLab testbed of 25 smartphones. Our study reveals that computations over SmartTrace+ result in substantial energy conservation; in addition, results can be computed faster than competitive approaches. [ABSTRACT FROM PUBLISHER]