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Recommender Systems are tools that improve how users find relevantinformation in web systems, so they do not face too much information. In orderto generate better recommendations, the context of information should be usedin the recommendation process. Context-Aware Recommender Systems were created,accomplishing state-of-the-art results and improving traditional recommendersystems. There are many approaches to build recommender systems, and two of themost prominent advances in area have been the use of Embeddings to representthe data in the recommender system, and the use of Deep Learning architecturesto generate the recommendations to the user. A systematic review adopts aformal and systematic method to perform a bibliographic review, and it is usedto identify and evaluate all the research in certain area of study, byanalyzing the relevant research published. A systematic review was conducted tounderstand how the Deep Learning and Embeddings techniques are being applied toimprove Context-Aware Recommender Systems. We summarized the architectures thatare used to create those and the domains that they are used.

A Systematic Review on Context-Aware Recommender Systems using Deep Learning and Embeddings