A Location-Based Movie Recommender System Using Collaborative Filtering
This work addresses the need for more accurate movie recommendations for users by integrating location data, representing an incremental improvement over traditional collaborative filtering methods.
The paper tackled the problem of poor recommendation accuracy in collaborative filtering by incorporating user location data into a movie recommender system, resulting in enhanced accuracy and quality of recommendations as demonstrated through experiments on real datasets.
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 location-based 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.