## 1 Introduction  
Ever since traveling has become more accessible, people have been relying a lot on the location-based recommender system for places to visit during their voyage. As a result, location-based recommender system has gained much attention in the areas of commercial and in the research communities in recent years. Researchers have investigated new approaches to develop novel systems that are able to generate high-quality and personalized recommendations for users. However, in most previous research, location-based recommender system have generated recommendations without considering the current location of the target user. Such systems could recommend places that are far from the location of the target user, and thereby it can be impossible for the user to reach the places at that moment. There are only a handful of state-of-art research that have been proposed to incorporate geographical and social influence into collaborative filtering approach when generating location recommendations [1; 2; 3; 4; 5; 6; 7]. However, those research have not seriously regarded the location of the target user. Suppose a case of one user, for example, who lives in Malaysia and travels abroad to Singapore for vacation. From the original intention of collaborative filtering, users who exhibit similar location visiting behavior to the target user (also known as similar users) are chosen to provide clues for making a recommendation. Since human mobility exhibits geographical locality, most of the similar users are probably living in Malaysia because they have visited various locations that the target user has. As recommendations are generated considering the locations visited by the similar users (who may have never been in Singapore), the recommended locations may be very far from the target user current location and may also not be reachable by the target user at that particular moment. In summary, those previous collaborative filtering approaches may recommend the same location, no matter where the user is currently located in the world. Thus, the systems with those approaches might fail to consider the social aspects that influence a user in the location-based recommender system. In this paper, we study the issues of generating location recommendations for users who travels abroad (users who are far away from their home region) by taking into account the user preferences, social connections, and geographical proximity.  
Recently, many studies have explored the application of social relationship amongst the users in recommender systems to enhance the effectiveness of recommendation techniques [1; 4; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. The main ideas of those research are incorporating the trust and interest similarity carried along in social relationships amongst friends to improve personalized search and recommendations. However, as aforementioned, these prior works mainly focus on conventional ways for recommendations. There are only a handful of studies that have explored the use of local experts (natives) in a geospatial range which matches the user's preferences by a preference-aware candidate selection algorithm and then infer a score of the candidate locations based on the opinions of the selected local experts which offers good efficiency of providing location recommendations.  
By exploring the strong social ties between friends and natives, we have proposed a location-based recommender system called _Friend-And-Native-Aware Approach For Collaborative Filtering_ (FANA-CF) for location-based recommendation based on collaborative ratings of commonly visited places made by social friends and the local experts of a certain range of geospatial location, and we perform extensive and systematic experimental analysis on FANA-CF. The evaluation results from benchmark datasets have shown that FANA-CF holds comparable recommendation effectiveness against the compared algorithms.