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Collaborative filtering approach is widely used in the area of recommender systems. In fact, its predictive accuracy is supported by a large amount of additional information available on the intern...

In the last decade, social-based recommender systems have become the best way to resolve a user's cold start problem. In fact, it enriches the user's model by adding additional information provided from his social network. Most of those approaches are based on a collaborative filtering and compute similarities between the users. The authors' preliminary objective in this work is to propose an innovative context aware metric between users (called contextual influencer user). These new similarities are called C-COS, C-PCC and C-MSD, where C refers to the category. The contextual influencer user model is integrated into a social based recommendation system. The category of the items is considered as the most pertinent context element. The authors' proposal is implemented and tested within the food dataset. The experimentation proved that the contextual influencer user measure achieves 0.873, 0.874, and 0.882 in terms of Mean Absolute Error (MAE) corresponding to C-cos, C-pcc and C-msd, respectively. The experimental results showed that their model outperforms several existing methods.

A recommender system (RS) is a subclass of information systems. It aims at providing the most relevant items (music, film…) that are preferred to each user. Several recommendation algorithms have been proposed in the literature and a comparison across their experimental results is necessary to evaluate the best algorithm. This paper presents a framework for presenting, developing and evaluating a recommender system. We preserve that this approach could play a vital role in elaborating an architecture and implementation of this type of systems. The proposed model presents the process of preparing the data set, whether rating or social data. It also includes a suite of state-of-the-art algorithms. The specificity of our architecture is the possibility of developing four kinds of recommender systems that are baseline, social, contextual and socio-contextual recommender system.

Nowadays, the Internet remains the essential source of access to scientific and technical information. In some languages, including Arabic, the means used to search for information do not seem to perform as well as in other languages. This deficiency is probably due to the late introduction of the Internet into the Arabization of the scientific and technological world, on the one hand, and to advances in the development of Arabic-language digital processing, on the other. This article intends to identify and explain the limitations and problems of searching for video in Arabic. For this reason, we propose an Arabic video indexing and retrieval system. In the indexation phase, our method is a combination between low-level and height-level indexation. In the retrieval phase, we put the user at the center of the research process. This goal is achieved by including a relevance feedback mechanism. The proposed new system is tested and prove that is perform well in a large scale database.

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.

Semi-automatic or automatic image indexation emerged because manual image indexation is slow and tedious. Generally, this first indexation is used as part of a content-based image retrieval system (CBIR). To have a powerful CBIR system, it is necessary to be concerned with three main facets: 1) the choice of the descriptors (based on shape, colour and texture and/or a combination between them); 2) the process of indexation and finally; 3) the retrieval process. In this work, we focus mainly on an indexing based on genetic algorithm and particle swarm optimisation (PSO) algorithm. We chose an optimal combination of colour, shape and texture (PCM: powerful combination method) descriptors. The fruit of our research work is implemented in a system called image search engine (ISE) which showed a very promising performance. In fact, the performance evaluation of the PCM method of our descriptors combination showed upgrades of the average precision metric from 66.6% to 89.30% for the 'food' category colour histogram, from 77.7% to 100% concerning CCV for the 'flower' category, and from 44.4% to 87.65% concerning the co-occurrence matrix for the 'building' category using the Corel dataset. Likewise, our ISE system showed much more interesting performance compared to what was shown in previous works.

Concept detection has been considered a difficult problem and has attracted the interest of the content-based multimedia retrieval community. This detection implies an association between the concept and the visual content. In other words, the visual characteristics extracted from the video. This includes taking knowledge about the concept itself and its context. This work focuses on the problem of concept detection. For that, several stages are elaborated: first, a method of extraction and semi-automatic annotation of the video plans for the training set is proposed. This new method is based on the genetic algorithm. Then, a preliminary concept detection is carried out to generate the visual dictionary (BoVS). This second step is improved thanks to a noise reduction mechanism. This article's contribution has proven its effectiveness by testing it on a large dataset.

International audience