Abra iTour: A Semantic Web Recommender Using Hybrid Algorithm
Arpee M. Callejo1, Amando P. Singun2

1Arpee M. Callejo, College of Business Administration and Accountancy, University of Northern Philippines- Vigan City, Ilocos Sur.
2Dr. Amando P. Singun, Jr. Higher College of Technology, Muscat, Oman.
Manuscript received on 02 June 2019 | Revised Manuscript received on 27 June 2019 | Manuscript Published on 04 July 2019 | PP: 97-103 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10190681S419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Information on the web is increasing at an exponential rate. This has resulted in a myriad of choices that are available for users on the web giving complex processes of the world’s largest database, the Internet. This has also given birth to the development of data filtering algorithms used in recommender technologies that help users find their best decision from the large unstructured database of the World Wide Web (WWW). Recommender systems have been widely used in ecommerce websites like Lazada, Amazon and other popular websites like YouTube, Spotify, Linkedin, Facebook, and Instagram. Thus, the researchers came up with a study on the development of a recommender system. This study is a research and development of a recommender system for the province of Abra, Philippines, titled “Abra iTour: A Semantic Web Recommender Using Hybrid Algorithm”. The system adopted a hybrid algorithm, a combination of Collaborative and Content-based filtering algorithms to extract data for the recommendation lists that are offered to the users of the system. IT experts assessed the extent on the level of efficiency of the hybrid algorithm with a 4.59 rating described as Very Great Extent. Thus, the hybrid algorithm used by the system is proven. The recommender system ISO 25010 Software Quality Standards evaluation acquired an overall weighted mean of 4.52, Very Great Extent. This implies that the recommender system is ready for deployment and implementation.
Keywords: Hybrid Filtering Algorithm, Machine-Learning, Recommender Systems, Web Mining.
Scope of the Article: Web Mining