Abstract
Recently, with the presence of a lot of information and the emergence of many programs, sites and companies that provide items to customers like Amazon for products or Netflix for movies …, it was necessary to exploit this data to achieve a quantum leap in the world of technology and specially do not leave the customer confused in the item to be chosen among other huge options, so many of sciences that are interested in the field of Big data and using the large information to meet the needs of users intervened to improve the area of recommendation such as data science, machine learning…. however there is one solution to give suggestions for customers is recommender systems. Recommender systems is a useful information filtering tool for guiding users in a personalized way of discovering products or services they might be interested in from a large space of possible options. It predicts interests of users and makes recommendation according to the interest model of users. On one hand, there is a traditional recommender systems recommend items based on different criteria of users or items like item price, user profile …on another hand we have recommender systems using deep learning techniques even if not been well explored yet. In this article, we first introduce different kinds of the most famous category of recommender systems and focus on one type to do movies recommendations and then make a quantitative comparison.
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We would like to express our sincere thanks to members of new technology trends our research laboratory. Their advices and comments are gratefully acknowledged.
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Afoudi, Y., Lazaar, M., Al Achhab, M. (2019). Collaborative Filtering Recommender System. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_30
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