In multidimensional item response theory, it is possible for the estimate of a subject’s ability in some dimension to decrease after they have answered a question correctly. When this occurs, the fairness of the estimate may be called into question. This paper investigates how and when this form of non-monotonicity occurs. We demonstrate that many response models and statistical estimates can p...

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users a...

one of the organizations’ concerns is their awareness about the degree of readiness for implementing social intranet. in response to this problem, a 48 item questionnaire was developed. fars regional electric company consisting of 820 human resources was selected as statistical population. by systematic random method, 262 personnel were determined as sample and the developed questionnaire were ...

BACKGROUND Social media can promote healthy behaviors by facilitating engagement and collaboration among health professionals and the public. Thus, social media is quickly becoming a vital tool for health promotion. While guidelines and trainings exist for public health professionals, there are currently no standardized measures to assess individual social media competency among Certified Healt...

Collaborative filtering based recommendation methods focus on user-item information for modeling the user interest. However, in social networks the user interest is influenced by other user interests in the local social circle of the active user. In this paper, considering the homophily of relation to similar interests and similar friends, we propose a social item recommendation framework (SocI...

We introduce Poisson Matrix Factorization with Content and Social trust information (PoissonMF-CS), a latent variable probabilistic model for recommender systems with the objective of jointly modeling social trust, item content and user’s preference using Poisson matrix factorization framework. This probabilistic model is equivalent to collectively factorizing a non-negative user–item interacti...