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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In recent years, the explosive growth of multimedia databases and digital libraries reveals crucial problems in indexing and retrieving images, what led us to develop our own approach. Our proposed approach TAD consists in disambiguating web queries to build an adaptive semantic for diversity-based image retrieval. In fact, the TAD approach is a puzzle constituted by three main components which are the TAWQU (Thesaurus-Based Ambiguous Web Query Understanding) process, the ASC (Adaptive Semantic Construction) process and the DR (Diversity-based Retrieval) process. The Wikipedia pages represent our main source of information. The NUS-WIDE dataset is the bedrock of our adaptive semantic. Actually, it permits us to perform a respectful evaluation. Fortunately, the experiments demonstrate promising results for the majority of the twelve ambiguous queries.

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