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This paper introduces an augmented reality-based framework (called AugmentedBook) for e-learning that allows the creation of collaborative notes, illustrative media (i.e. video, 2D or 3D image, audio) for mobile devices or Google glass. The augmented content can be added to real-world educational support to make it more comprehensive, interactive and collaborative. In this platform, students and teachers can add collaborative notes to any part of the educational support system. They can also find illustrative media and indicate the pertinence of the result. Using our AugmentedBook platform, students can also download the enriched support using a mobile device. Our framework solves the problem of standard integration of augmented reality applications in education, offering a distributed framework which is e-learning compliant.

Recommender systems suggest the most appropriate items to users in order to help customers to find the most relevant items and facilitate sales. Collaborative filtering recommendation algorithm is the most successful technique for recommendation. In view of the fact that collaborative filtering systems depend on neighbors as the source of information, the recommendation quality of this approach depends on the neighbor’s selection. However, selecting neighbors can either stem from similarity or trust metrics. In this paper, we analyze these two types of neighbor’s selection metrics used in the field of recommendation in the literature. For each type, we first define it and then review different proposed metrics.

Nowadays, internet has offer an overabundance of available information. In social networks, users confront gigantic number of items. To overcome this phenomenon, known as information overload, recommender systems are intended to filter information and help users to make their choice. Many models based collaborative filtering have been used in the literature to solve the problem of recommendation. Among these models, latent factor model has become the most popular due to his performed results of accuracy. This work is part of research into Recommender System domain and aims to present a detailed explication on works based latent factor model. We first describe a general view of this model. Its realization in field of recommendation is next presented. A detailed study on different steps is then exposed. The most important works that have been developed are then presented. To the author’s knowledge, there has been no work that tries to explain in detail how latent factor model is applied to Recommender Systems.