Tourism Recommender System using Weighted Parallel Hybrid Method with Singular Value Decomposition - Yoan Amri Akbar; zk abdurahman baizal; Agung Toto Wibowo (School of Computing, Telkom University, 2021)
Presently, we often get suggestions for recommendations for tourist attractions from various sources such as the internet, magazines, newspapers, or travel agencies. Because there is numerous information, tourists become difficult to determine the tourism destination that suits their wishes. We created a tourism recommender system that can provide information in the form of recommendations for tourist attractions by the preference of tourists. The method used is a hybrid method that combines several recommendation methods, which are Content-Based Filtering (CB) and Collaborative Filtering (CF). We use tourism data of Lombok Island, West Nusa Tenggara, which will be taken from the TripAdvisor site. We apply the Singular Value Decomposition algorithm on CF and CB. The Hybrid Weighted Parallel Technique is used for Hybrid Method. The results of the experiment show that the weighting technique hybrid method provides higher prediction accuracy than when undergoing the recommender system method separately. The average results of Mean Square Error were obtained 0.7275 (CF), 0 .4583 (CB), and 0.2548 (Hybrid Method). The result indicates that the Hybrid Method with the Weighting Technique has the highest accuracy of another method.