## 5 Conclusions and Future Work  
Nowadays, recommender systems can be found in various modern applications [25], and the location-based recommender system is one of the applications that has gained popularity amongst modern applications [26]. In this paper, we have proposed a location-based recommender system, the Friend-And-Native-Aware Approach For Collaborative Filtering (FANA-CF) which contains two approaches, FANA-IBCF and FANA-UBCF.  
This proposed approach mainly focuses on two groups of users' ratings in order to generate recommendations to the target user. The two groups of users are the friends (users who are linked to the target user socially) and the natives (users who originate from the location that the target user wishes to go to).  
Figure 7: Graphical plots of averages and standard deviations from 5-fold cross validation for MAE, RMSE, and nDCG metrics of IBCF, UBCF, PersMean, and our proposed approach, FANA-CF.  
The reason why we have chosen to use friends is because we believe that users that are linked to the target user have the same preferences, whereas the reason why we have chosen natives is because we believe that users originating from the location that the target user wishes to go know the best places to visit.  
Although recommender systems have been popular in both industry and academia, there are only a handful of studies that were carried out on a recommender system that focuses on these two groups of users, which we strongly believe to be able to give personalized and high-quality recommendations.  
The implementation was carried out in order to test the performance, and the performance evaluations were conducted for the existing approaches and our proposed approach. The result of the performance evaluations showed that our proposed approach that uses two groups of users' ratings (friends and natives) to generate recommendations has a slightly lower error rate (higher accuracy) in generating recommendations and also manages to generate slightly higher-quality recommendations than the existing IBCF, UBCF, and PersMean approaches that use every user's rating to generate recommendations.  
The future work will be focused on implementing other recommender methods in order to generate even higher quality recommendations,including those with deep learning techniques [27]. Besides that, we also plan to add more attributes, such as time, in order to generate high-quality recommendations. Finally, we want to investigate theoretically and empirically the effect of dataset size on efficiency of collaborative filtering recommender systems [28].  
Conceptualization, A.L.C.Y.; methodology, A.L.C.Y.; software, A.L.C.Y.; validation, A.L.C.Y.; formal analysis, A.L.C.Y.; investigation, A.L.C.Y.; resources, D.-K.K.; data curation, A.L.C.Y.; writing--original draft preparation, A.L.C.Y.; writing--review and editing, D.-K.K.; visualization, A.L.C.Y.; supervision, D.-K.K.; project administration, D.-K.K.; funding acquisition, D.-K.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A02050166).  
Not applicable.  
The authors wish to thank members of the Dongseo University Machine Learning/Deep Learning Research Laboratory and the anonymous referees for their helpful comments on earlier drafts of this paper.  
The authors declare no conflict of interest.