UMN Knowledge Center

Applying Neural Network Model to Hybrid Tourist Attraction Recommendations

Indriana, Marcelli and Hwang, Chein-Shung (2014) Applying Neural Network Model to Hybrid Tourist Attraction Recommendations. Ultimatics: Jurnal Teknik Informatika, 6 (2). ISSN 2581-186X

Full text not available from this repository.
Official URL:


Recently, recommender systems have been developed for a variety of domains. Recommender systems also can be applied in tourism industry to help tourists organizing their travel plans. Recommender systems can be developed by a variety of different techniques such as Content-Based filtering (CB), Collaborative filtering (CF), and Demographic filtering (DF). However, the uses of these techniques individually will have some disadvantages. In this research, we propose a hybrid recommender system to combine the predictions from CB, CF and DF approaches using neural network model. Neural network model will learn by processing a training dataset, comparing the network’s prediction for each dataset with the actual known target value. For each training dataset, the weights are modified to minimize the mean-squared error between the network’s prediction and the actual target value. The experimental results showed that the neural network model outperforms each individual recommendation techniques.

Item Type: Article
Uncontrolled Keywords: Colaborative Filtering, Content-based filtering, Data Mining, Demographic Filtering, Hybrid Recommender System, Neural Network
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods (3D Graphics, Digital Video, Data Mining, Augmented Reality)
500 Science and Mathematic > 500 Science > 507 Education, Research, Related Topics
Divisions: Fakultas Teknik Informatika > Program Studi Sistem Informasi
Depositing User: mr admin umn
Date Deposited: 16 Nov 2021 05:55
Last Modified: 27 Jan 2022 02:14

Actions (login required)

View Item View Item