The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is no...

BACKGROUND The development of a mouse model as an in vivo pathogenicity screening tool for Brachyspira spp. has advanced the study of these economically important pathogens in recent years. However, none of the murine models published to date have been used to characterize the clinical signs of disease in mice, instead focusing on pathology following oral inoculation with various Brachyspira sp...

OBJECTIVE To ascertain the effect of endoscopic sinus surgery (ESS) on lung function, intravenous (IV) antibiotic use, and hospitalization in adults with cystic fibrosis (CF). DESIGN Retrospective analysis. SETTINGS Tertiary care center. PATIENTS Fifteen adults with a diagnosis of CF undergoing ESS between March 1, 2006, and June 31, 2008. MAIN OUTCOME MEASURES Twelve-month preoperative...

Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative research methods, but non-linear probabilistic beyond limit linear model capacity. For example, variational autoencoders (VAEs) have been extensively CF, and achieved excellent results. Aiming at problem prior distribution for latent codes VAEs traditional CF t...

This paper uses integrated Data Envelopment Analysis (DEA) models to rank all extreme and non-extreme efficient Decision Making Units (DMUs) and then applies integrated DEA ranking method as a criterion to modify Genetic Algorithm (GA) for finding Pareto optimal solutions of a Multi Objective Programming (MOP) problem. The researchers have used ranking method as a shortcut way to modify GA to d...

In recent years, the parameterized complexity approach has lead to the introduction of many new algorithms and frameworks on graphs and digraphs of bounded clique-width and, equivalently, rank-width. However, despite intensive work on the subject, there still exist well-established hard problems where neither a parameterized algorithm nor a theoretical obstacle to its existence are known. Our a...

Page Rank is extensively used for ranking web pages in order of relevance by mostly all search engines world-wide. There are many algorithms for page ranking such as Google Page Rank algorithm, Hyperlink-Induced Topic Search (HITS) algorithm etc. Some search engine uses link structure based page ranking algorithm while some uses content based. The page ranking algorithm reflects the popularity ...

 Abstract—KNN classification finds k nearest neighbors of a query in training data and then predicts the class of the query as the most frequent one occurring in the neighbors. This is a typical method based on the majority rule. Although majority-rule based methods have widely and successfully been used in real applications, they can be unsuitable to the learning setting of skewed class distr...

Based on the user-item bipartite network, collaborative filtering (CF) recommender systems predict users’ interests according to their history collections, which is a promising way to solve the information exploration problem. However, CF algorithm encounters cold start and sparsity problems. The trust-based CF algorithm is implemented by collecting the users’ trust statements, which is time-co...