Abstract-Due to the revolutionary advances of deep learning achieved in the field of computer vision, object recognition and natural language processing, the deep learning gained much attention. The recommendation task is influenced by the deep learning trend which shows its significant effectiveness. The deep learning based recommender models provides a better detention of user preferences, item features and users-items interactions history. In this paper, we provide a recent literature review of researches dealing with deep learning based recommendation approaches which is preceded by a presentation of the main lines of the recommendation approaches and the deep learning techniques. Then we finish by presenting the recommendation approach adopted by the most popular video recommendation platform YouTube which is based essentially on deep learning advances.

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Given the continued growth in the number of documents available in the social Web, it becomes increasingly difficult for a user to find relevant resources satisfying his information need. Personalization seems to be an efficient manner to improve the retrieval engine effectiveness. In this paper we introduce a personalized image retrieval system based on user profile modeling depending on user's context. The context includes user comments, rates, tags and preferences extracted from social network. We adopt a fuzzy logic-based user profile modeling due to its flexibility in decision making since user preference are always imprecise. The user has to specify his initial need description by rating concepts and contexts he is interested in. Concepts and contexts are weighted by the user by associating a score and these scores will infer in our fuzzy model to predict the preference degree related to each concept for such context and return the preference degree. Relying on the score affected for each concept and context we deduce its importance to apply then the appropriate fuzzy rule. As for as the experiments, the advanced user profile modeling with fuzzy logic shows more flexibility in the interpretation of the query.

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