## 2 Related Work
The authors have validated the proposed ideas and have evaluated the FCF family techniques through comprehensive experimentation. The evaluation results showed that the family of FCF approaches has comparable recommendation effectiveness against other state-of-the-art recommendation approaches, while incurring significantly lower computational overheads. Location-based and Preference-Aware Recommendation Using Sparse Geo-social Networking Data by [PERSON] et al. [12] has presented a location-based and preference-aware recommender system that offers users a set of venues within a geospatial range with the consideration of: (a) users' personal preferences which were learned automatically from users' location history, and (b) social opinions, which were mined from the location histories of the local experts. This recommender system can facilitate users with information on not only the areas they reside but also the places that are completely unfamiliar to them. Since a user can only visit a limited number of locations within a limited amount of time and the user location matrix is very sparse, thus leading to a big challenge for traditional collaborative filtering-based location recommender system. The problem becomes even more challenging when people travel to a new city in which they have not visited before. The authors have evaluated their system with a large-scale real dataset collected from Foursquare. Following that, the results have confirmed that the method offers more effective recommendations than baselines, while having efficiency in providing the location recommendations. [PERSON] et al. [10] exploited the idea of object typicality from cognitive psychology and proposed a novel typicality-based collaborative filtering recommendation method named TyCo. TyCo finds \"neighbours\" of users based on user typicality degrees in user groups. User Preference, Proximity and Social-Based Collaborative Filtering (UPS-CF) by [PERSON] et al. [4], has incorporated users' preferences, social connections, and geographical proximity to give location recommendations for mobile users which for most location-based recommender system generates recommendations without considering where the target user is currently located which may generate recommendations that are far from the target user currently located. The authors have conducted extensive experiment to evaluate their proposal and have compared it with CF variants and the baseline approaches using the dataset from Foursquare and Gowalla. The authors have shown that UPS-CF outperformed all other comparing approaches. The authors have also proven that the effectiveness does not degrade for out-of-town users. They have also found that for in-town users, similar users are important, while social friends become even more important for out-of-town users. [PERSON] and [PERSON] [13] proposed the idea of using recommendations from friends and natives. Their idea of using \(\alpha\) for balancing between two measures is inspired from elastic net regularization [18]. In this paper, we extend their idea to design a collaborative filtering recommendation framework, the Friend-And-Naitve-Aware Approach for Collaborative Filtering (FANA-CF). We have validated our approach by comprehensive experiments using real datasets collected from Foursquare. [PERSON] et al. [6] presented a new user similarity model to improve the recommendation performance. Their model considers the local context information of user ratings and the global preference of user behavior. They demonstrated their model on three real data sets and showed the superiority of their model. The Geographical Sparse Additive Generative Model for Spatial Item Recommendation, Geo-SAGE, proposed by [19] for out-of-town and home-town recommendations. It considers both the user's personal interests and the preferences of the crowd in the target location, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items.