Open Access

ARTICLE

Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN

Mohanad Al-Ghobari¹, Amgad Muneer^2,*, Suliman Mohamed Fati³

1 School of Information Technology, Asia Pacific University, Kuala Lumpur, 57000, Malaysia
2 Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32160, Malaysia
3 Department of Information Systems, Prince Sultan University, Riyadh, 11586, Saudi Arabia

* Corresponding Author: Amgad Muneer. Email:

(This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 69(2), 1553-1570. https://doi.org/10.32604/cmc.2021.016348

Received 30 December 2020; Accepted 08 April 2021; Issue published 21 July 2021

Abstract

Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites, accommodation, and food according to their interests. This objective makes it harder for tourists to decide and plan where to go and what to do. Aside from hiring a local guide, an option which is beyond most travelers’ budgets, the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews. Therefore, this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue. Accordingly, this study proposes location-aware personalized traveler assistance (LAPTA), a system which integrates user preferences and the global positioning system (GPS) to generate personalized and location-aware recommendations. That integration will enable the enhanced recommendation of the developed scheme relative to those from the traditional recommender systems used in customer ratings. Specifically, LAPTA separates the data obtained from Google locations into name and category tags. After the data separation, the system fetches the keywords from the user’s input according to the user’s past research behavior. The proposed system uses the K-Nearest algorithm to match the name and category tags with the user’s input to generate personalized suggestions. The system also provides suggestions on the basis of nearby popular attractions using the Google point of interest feature to enhance system usability. The experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to the reviewed recommendation applications.

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Citations