Analysis of User Generated Content Based on a Recommender System and Augmented Reality

Recommender systems have demonstrated to be very useful in various research areas such as education, e-government, e-commerce, and collaborative and entertainment applications. These systems are based on a set of preferences that aim to help users make decisions by offering different items or services that might interest them. However, by using traditional search approaches, the user often obtains results that do not match the desired interests. Thus, a new search approach is required to use semantic-based retrieval techniques to generate conceptually close results to user preferences. In this paper, a methodology to retrieve information about user preferences based on a recommender system and augmented reality is proposed. As a case study, an Android mobile application was implemented, considering augmented reality to recommend multiplex cinemas that are generated from the genres of movies preferred by users and their geographical location at the time of the search.