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.Abstract
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 |
|---|---|
| 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 500 Science and Mathematic > 500 Science > 507 Education, Research, Related Topics |
| Divisions: | Faculty of Engineering & Informatics > Information System |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 16 Nov 2021 05:55 |
| Last Modified: | 27 Jan 2022 02:14 |
| URI: | https://kc.umn.ac.id/id/eprint/19063 |
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