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.
A method to simplify the calculation in the process of measuring graph similarity is proposed, where lots of redundant operations are avoided in order to quickly obtain the initial tickets matrixIn this proposal, the ...
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ISBN:
(纸本)9783319483535
A method to simplify the calculation in the process of measuring graph similarity is proposed, where lots of redundant operations are avoided in order to quickly obtain the initial tickets matrixIn this proposal, the element value of the initial tickets matrix is assigned to 1 when it is positive in corresponding position of the paths matrix at the first timeThe proposed method calculates the initial tickets matrix value based on the positive value in the paths matrix in a forward and backward wayAn example is provided to illustrate that the method is feasible and effective.
In this letter, we suggest a novel Object Shrunken (OS) algorithm to handle the image classification task. Unlike the prior art, this letter considers the foreground or the object location in the image for more discri...
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In this letter, we suggest a novel Object Shrunken (OS) algorithm to handle the image classification task. Unlike the prior art, this letter considers the foreground or the object location in the image for more discriminative image-level representation. The OS algorithm suggests a straightforward procedure to box the object location. It first proposes a Weighted Local Outlier Factor (WLOF) to remove all the interest point outliers, and then positions the object location in terms of the distribution of the rest interest points. We evaluate the proposed algorithm on the well-known dataset Caltech-101. The resulting OS algorithm outperforms the state-of-art approaches in the image classification task.
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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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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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.
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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Medoids-based fuzzy relational clustering generates clusters of objects based on relational data, which records pairwise similarity or dissimilarities among objects. Compared with single-medoid based approaches, multi...
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Most of the microarray expression data have tens of thousands of genes but very small number of samples. Feature selection has been widely used to extract the subset of informative genes. Though many feature selection...
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