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, 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.

This paper proposes a context-aware semantic planning graph technique for Web services composition. We first use an ontology based context model for extending Web services descriptions with information about the most suitable context for its use. Then, we transform the composition problem into a semantic context aware graph planning problem to build a set of best composed Web services based on user’s context. The construction of the planning graph is based on semantic context-aware Web service discovery. This allow, for each step of the construction, to add most suitable Web services in terms of semantic compatibility between the services parameters, and their context similarity with the user’s context. In the backward search step, semantic and contextual similarity scores are used to find composed Web services list. Finally, in the ranking step, a score is calculated for each candidate solution and a set of ranked solutions is returned to the user.