Abstract
Available recommender systems mostly provide recommendations based on the users’ preferences by
utilizing traditional methods such as collaborative filtering which only relies on the similarities between
users and items. However, collaborative filtering might lead to provide poor recommendation because it
does not rely on other useful available data such as users’ locations and hence the accuracy of the
recommendations could be very low and inefficient. This could be very obvious in the systems that locations
would affect users’ preferences highly such as movie recommender systems. In this paper a new locationbased movie recommender system based on the collaborative filtering is introduced for enhancing the
accuracy and the quality of recommendations. In this approach, users’ locations have been utilized and
take in consideration in the entire processing of the recommendations and peer selections. The potential of
the proposed approach in providing novel and better quality recommendations have been discussed
through experiments in real datasets.
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