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In the field of recommender systems, the Beer & Nappies is a famous story, which reveals the latent relationships between different categories of items. Though matrix factorization (MF) has demonstrated its great effectiveness in most previous work, it neglects the co-occurrences of items selected by individuals. In most MF-based models, the latent preferences of users (or the latent categories...
With the rapid development of Location-based Social Network (LBSN) services, a large number of Point-Of-Interests (POIs) have been available, which consequently raises a great demand of building personalized POI recommender systems. A personalized POI recommender system can significantly assist users to find their preferred POIs and help POI owners to attract more customers. However, it is very challenging...
Analog behavioral models are widely used to reduce the complexity in hierarchical analog circuit design and verification. In the presence of process variations and atomic-level fluctuations, however, these models have to be extended to take variability into account. In this paper, we present a probabilistic solution that treats the behavioral model coefficients as multidimensional random variables...
Bayesian Networks are probabilistic models of data that are useful to answer probabilistic queries. Existing algorithms use either local measures of deviation from independence or global likelihood measures. They are based on probabilistic correlation, so the directionality of the model lacks the causal meaning as we expected. We tackle this problem from a new perspective using causality, which is...
In this paper we present a data-driven approach for the problem of approximate data collection in sensor networks within the disjoint clique framework. The objective is to obtain estimations of attribute values from sensor nodes within certain error bounds while spending minimal amount of energy. Chu et al.~\cite{chu-icde06} showed that a disjoint clique model is a natural and effective way of exploiting...
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