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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.

International audience

International audience; This paper proposes a new approach that uses social networks and common sense deduction rules to adapt the description tags of the photos for the current viewer. We exploit social graphs to enrich the tags associated to the concerned persons in the photo by following the different links between people (i.e. viewer and captured people in the photos). The main contributions of our work are: (i) addition of a more meaningful tagging layer for photos, making tags dynamic and auto-adaptable thanks to the automatic identification of the social context of the visualization. (ii) Due to this dynamics, the search in the social graphs is optimized using a data mining technique. (iii) we propose a new visualization metaphor for the tagging layer to manage users' feedback. We also describe a system architecture and an experimental study that shows significant improvements of the tagging process and execution times on a dataset containing triples in a FOAF graph.