In this paper, A new distribution called Exponential Lomax distribution is introduced. It is seemed that the parameter values of our new distribution are depending on decreasing and upside-down bathtub failure rate function. Also, the statistical properties of this model are studied, such as, quantiles, moments, mean deviation. Moreover, maximum likelihood estimators of it ' s parameters are discussed. Finally, the procedure is illustrated by real data set. It is shown that the introduced model is more competitive than other models.

No abstract

Abstract. Recommender systems are needed to find food items of one's interest. We review recommender systems and recommendation methods. We propose a food personalization framework based on adaptive hypermedia. We extend Hermes framework with food recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.

In this paper, a new two parameters model is introduced. We called it the inverse flexible Weibull extension (IFW) distribution. Several properties of this distribution have been discussed. The maximum likelihood estimators of the parameters are derived. Two real data sets are analyzed using the new model, which show that the new model fits the data better than some other very well known models.

The classical bivariate F distribution arises from ratios of chi-squared random variables with common denominators. A consequent disadvantage is that its univariate F marginal distributions have one degree of freedom parameter in common. In this paper, we add a further independent chisquared random variable to the denominator of one of the ratios and explore the extended bivariate F distribution, with marginals on arbitrary degrees of freedom, that results. Transformations linking F , beta and skew t distributions are then applied componentwise to produce bivariate beta and skew t distributions which also afford marginal (beta and skew t) distributions with arbitrary parameter values. We explore a variety of properties of these distributions and give an example of a potential application of the bivariate beta distribution in Bayesian analysis.