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.
Highlights
The recommender system is a necessity and a popular technology that, by collecting data from activities and inclinations, gains the interest of its customer from a set of data such as movies, hardware, clothing, etc. and makes offers to various customers
This study tends to develop a recommendation system using the pre-filtering approach by DBScan clustering and Haversine criterion and to improve accuracy of the proposed model using asymmetric similarity criterion in collaboration filtering approach
This matrix is formed using the rates that users gave to different places on the Flickr tourism social network
Summary
The recommender system is a necessity and a popular technology that, by collecting data from activities and inclinations, gains the interest of its customer from a set of data such as movies, hardware, clothing, etc. and makes offers to various customers. Huge growth of content and the number of users in the internet world is undeniable For this reason, mechanisms were created to filter the information available on the Internet. Collaboration filtering came from the idea that people often get the best recommendations from people with the same taste. Collaboration filtering includes techniques for matching people with similar tastes and interests and providing recommendations based on that [1]. Recommending systems are increasingly used in the field of e-tourism. In this field, services such as travel advice, a list of points of interest that match the user's taste, and recommendations for tourist packages, etc. Recommendation systems available in the tourism industry offer the best to the user based on tourist destination, time limit and certain budget. Once the user makes her selection, the system links the list of specified destinations using the same parameter vector [2]
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