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With the economic and social progress of whole world, people are more interested in improving their living condition and life manner. In this situation, travelling has emerged as a promising way to achieve the goal of healthcare (Azrour, M., et al, 2020; Liu, Y., et al, 2021; Hazarika, B. B., 2021; Nitu P., 2022). Currently, travelling has become a crucial and significant part of human production and life. As a consequence, a number of travelling-related enterprises, business and services have emerged rapidly, such as ctrip.com, TripeAdvisor, and so on. Such travelling websites as well as their interactions with massive travelers have formed the so-called big traveling data (Huang H., 2022; Liu, Y., et al, 2021; Kou, H., 2021). Deep mining and analysis of such big traveling data are of positive significance for improving the traveling market (Kumari, R., 2021; Li, L., 2021). However, due to the big volume of POIs (Point of Interest) as well as their diverse categories, how to recommend appropriate POIs to potential travelers based on experience knowledge have become a practical and significant research topic for travel enterprises.
Among the massive candidate POIs in traveling market, country POIs have constituted an important part due to their distinct characteristics in reflecting specific country culture. Especially for the citizens who are not familiar with the country POIs or cultures, they often live in busy cities and therefore, are generally curious with beautiful and interesting country culture and scenes. Today, many citizens in China or abroad are willing to travel to nearby country POIs to loosen their body and nerve at their free time such as weekends and holidays. Therefore, it is becoming a necessity to develop a specific recommender system that targets country POIs market.