This paper presents the state of art techniques in recommender systems (RS). The various
techniques are diagrammatically illustrated which on one hand helps a naïve researcher in …

Abstract Gaussian Process Latent Variable Model (GPLVM), as a flexible bayesian non-
parametric modeling method, has been extensively studied and applied in many learning …

Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …

The new user cold start issue represents a serious problem in recommender systems as it
can lead to the loss of new users who decide to stop using the system due to the lack of …

In the neighborhood-based Collaborative Filtering (CF) algorithms, the user similarity has an
important effect on the result of CF. In order to evaluate the user similarity comprehensively …

Discussion groups are one of the most important elements of collaborative learning which
utilize recommender systems to improve their performance in several aspects. This type of …

Recommender systems are a type of solution to the information overload problem suffered
by users of websites that allow the rating of certain items. The collaborative filtering …

Recommender systems play a crucial role in mediating our access to online information. We
show that such algorithms induce a particular kind of stereoty**: if preferences for a set of …

Similarity-based algorithms, often referred to as memory-based collaborative filtering
techniques, are one of the most successful methods in recommendation systems. When …

It is difficult for the users to reach the most appropriate and reliable item for them among vast
number of items and comments on these items. Recommendation systems and …