Technical Journal, Vol. 16 No. 1, 2022.
Original scientific paper
https://doi.org/10.31803//tg-20210706082307
Developing a Location-Based Recommender System Using Collaborative Filtering Technique in the Tourism Industry
Iman Kianinezhad
orcid.org/0000-0001-7713-3416
; Department of Computer Engineering, Faculty of Engineering, University of Applied Sciences & Technology, Ahvaz, Iran
Mehdi Bayati
; Department of Computer Engineering, Faculty of Engineering, Karoon University, Ahvaz, Iran
Ali Harounabadi
; Department of Computer Engineering, Faculty of Engineering, Islamic Azad University Tehran Center Branch, Tehran, Iran
Donya Akbari
; Department of Computer Engineering, Faculty of Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
Abstract
The rapid growth of new information and products in the virtual environment has made it time consuming to acquire relevant information and knowledge amidst a vast amount of information. Therefore, an intelligent system that can offer the most appropriate and desirable among the large amount of information and products by following the conditions and features selected by each user should be essentially efficient. Systems that perform this task are called recommendation systems. Given the volume of social network data, challenges such as short-term processing and increased accuracy of recommendations are discussed in this type of system. Hence, it can perform processes faster with less error and can be effective in improving the performance of social recommending systems in improving the classification and clustering of information with the help of collaboration filtering methods. This study first develops an innovative conceptual model of a social network-based tourism recommendation system using Flicker network data. This model is based on 9 key components. The comparison show that the proposed method has an accuracy of 0.3% and a lower error rate.
Keywords
Collaborative Filtering Algorithm; DB Scan Clustering; Haversine; Multi-Factor Systems; Recommender Systems; Similarity Criteria; Tourism
Hrčak ID:
271929
URI
Publication date:
4.2.2022.
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