## 3 Proposed Approach  
The proposed recommender system approach, the Friend-And-Native-Aware Approach For Collaborative Filtering (FANA-CF), essentially focuses on two groups of users who are the friends (as illustrated in Figure 1) and the natives/local experts (as illustrated in Figure 2) to generate recommendations to the target user. As mentioned earlier, the reason we only use these two groups of users is because we believe that friends have the same preferences, while natives can recommend high-quality recommendations to the target user. We directly define friends from the social relations of users, and we define natives simply from the clustering of users' residential addresses. More precisely, in our implementation, we exploited a given social graph structure (in socialgraph.dat described in Table 2) prepared by domain experts. Note that our notion of friends and natives is different from the trust metric [20] in a trust aware system. The trust metrics is a more abstract concept which can be expressed as the trustworthiness of \"unknown\" users. A trust weight is estimated from the propagation of trust over the trust network. The resulting trust weight can be used in place of the similarity weight. Therefore, trust can be considered as an elaborated form induced from social relation; however, it is different from our notion of friends, because we directly measure friend relationships from the social network. Nativeness is different from trust because it is a concept that can be established from one user. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Work** & **Social Influence** & **Native’s Knowledge** \\ \hline TrustSVD [14], ATCF [7], [PERSON] et al. [15] & Fully Used & Not Used \\ UPS-CF [4], [PERSON] et al. [12], [PERSON] et al. [6], Geo-SAGE [19] & Not Used & Fully Used \\ FCF and GM-FCF. [1] & Fully Used & Partially Used \\ [PERSON] and [PERSON] [13] & Partially Used & Partially Used \\ TyCo [10] & Not Used & Partially Used \\ Our approach & Fully Used & Fully Used \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of related work and our approach in terms of using social influence from friends and local experts’ knowledge (natives). Figure 1: A graph of friends’ check-in activity in a location-based recommender system. By combining both groups, we believe that this proposed recommender system approach will produce personalized and high-quality recommendations. In this section, we will explain the overall system, each process inside it in terms of an algorithmic description, and modus operandi of the system in detail. In order to obtain the recommendations, ratings of a given item need to be calculated. The recommendation, \(\hat{r}\), for the given item of the target user, is calculated as follows:  
\[\hat{r}=\alpha\cdot\hat{p}_{af}+(1-\alpha)\cdot\hat{p}_{an} \tag{5}\]  
\(\hat{p}_{af}\) represents the prediction of recommendations based on target user \(a\) and friend users \(f\). \(\hat{p}_{an}\) represents the prediction of recommendations based on target user \(a\) and native users, \(n\). Both predictions are calculated using collaborative filtering (CF) method. \(\alpha\) is the weights that are given to each of the relative prediction \(\hat{p}_{af}\) and \(\hat{p}_{an}\). In UBCF, the prediction \(\hat{p}_{af}\) and \(\hat{p}_{an}\) are obtained through weighted sum of others' rating method, FANA-UBCF. The equations for both predictions in UBCF are as follows:  
\[\hat{p}_{af}=\frac{\sum_{f\in FNN}\left(r_{fx}-\bar{r}_{f}\right)\times sim_{ af}}{\sum_{f\in FNN}sim_{af}} \tag{6}\]  
\[\hat{p}_{an}=\frac{\sum_{n\in NNN}(r_{nx}-\bar{r}_{n})\times sim_{an}}{\sum_{ n\in NNN}sim} \tag{7}\]  
In Equation (6), \(f\in FNN\) represents user \(f\) as one of the elements in the set of friend's nearest neighbour (FNN). \(r_{fx}\) represents the rating of user \(f\) on item \(x\). \(\bar{r}_{f}\) is the mean rating of user \(f\). \(sim_{af}\) represents the similarities between target user \(a\) and friend user \(f\).