Traditional Collaborative Filtering has been one of the most widely used recommender systems, unfortunately it suffers from cold-start and data sparsity problems. With the development of social networks, more recommen...
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ISBN:
(纸本)9781467375931
Traditional Collaborative Filtering has been one of the most widely used recommender systems, unfortunately it suffers from cold-start and data sparsity problems. With the development of social networks, more recommendation systems are trying to generate more eligible recommendation through excavating users' potential preferences using their social relationships. Almost all social recommender systems employ only positive inter-user relations such as friendship or trust information. However, incorporating negative relations in recommendation has not been investigated thoroughly in literature. In this paper, we propose a novel model-based method which takes advantage of both positive and negative inter-user relations. We apply matrix factorization techniques and utilize both rating and trust information to learn users' reasonable latent preference. We also incorporate two regularization terms to take distrust information into consideration. Our experiments on real-world and open datasets demonstrate the superiority of our model over the other state-of-the-art methods.
In order to avoid the defect of lip identification about one single picture and enhance the accuracy of the recognition, this paper applies the dynamic lip identification and puts forward the method to calibrate the f...
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With the development of high-throughput microarray chip technology, there are a large number of microarray expression data, which have few samples compared to the genes of high dimensions. And in recent years, more an...
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A genome-wide association study (GWAS) typically involves detecting epistatic interactions of multiple genetic variants on the susceptibility of complex human diseases. As the most abundant source of genetic variation...
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A genome-wide association study (GWAS) typically involves detecting epistatic interactions of multiple genetic variants on the susceptibility of complex human diseases. As the most abundant source of genetic variation in human genome, the number of single nucleotide polymorphisms (SNPs) reaches millions in public datasets and SNP data collected from thousands of individuals in case-control studies may shed light on our understanding of epistatic interactions. Various methods have been proposed in previous literature for identifying genetic interactions, but they are infeasible for GWAS as biology data is too large, we here propose a method ‘epistasis group based on Bayesian inference’ (EGBI). EGBI applies a Bayesian marker partition model to investigate observed case-control data and computes the posterior distribution of each epistasis group that is associated with the disease via Markov chain Monte Carlo (MCMC). When applied to both simulated data and WTCCC type 1 diabetes data, EGBI successfully identified many known susceptible genes including CTLA4 and MHC and performed more powerfully than its competitors.
In order to detect lane rapidly and accurately, the integration of scanning and image processing algorithms (SIP) based on the fuzzy method is proposed. Further, combination of the proposed algorithm with an adaptive ...
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Fabric pattern is a folk art of artificial fabrics pattern design which includes apparels (known as clothes and hats), and crafts (known as carpets and tapestries). Although image processing, information retrieval and...
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Ant colony algorithm is a more effective way of solving traveling salesman problem (TSP). Ant colony algorithm adopts the distributed parallel computing mechanism;and it is easy to combine with other methods. Furtherm...
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In this paper, a differential evolution (DE) algorithm combined with Lévy flight is proposed to solve the reliability redundancy allocation problems. The Lévy flight is incorporated to enhance the ability of...
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Policy iteration,which evaluates and improves the control policy iteratively,is a reinforcement learning *** evaluation with the least-squares method can draw more useful information from the empirical data and theref...
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Policy iteration,which evaluates and improves the control policy iteratively,is a reinforcement learning *** evaluation with the least-squares method can draw more useful information from the empirical data and therefore improve the data ***,most existing online least-squares policy iteration methods only use each sample just once,resulting in the low utilization *** the goal of improving the utilization efficiency,we propose an experience replay for least-squares policy iteration(ERLSPI)and prove its *** method combines online least-squares policy iteration method with experience replay,stores the samples which are generated online,and reuses these samples with least-squares method to update the control *** apply the ERLSPI method for the inverted pendulum system,a typical benchmark *** experimental results show that the method can effectively take advantage of the previous experience and knowledge,improve the empirical utilization efficiency,and accelerate the convergence speed.
In this paper, we proposed a new image fusion scheme on spatial domain. The interest of the scheme is its real time. The framework contains two steps: saliency detection and coefficient selection based on the principl...
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