## 2 Related Work  
The collaborative filtering (CF) technique is widely used for recommender systems and many CF recommendation approaches [16] have been proposed. The CF can be classified into two categories, memory-based CF and model-based CF. Memory-based CF methods can be further classified as user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). UBCF first finds similar users based on their ratings of items, using a similarity measurement such as the Pearson correlation coefficient shown as follows:  
\[w_{a,u}=\frac{\sum_{i\in I}(r_{a,i}-\bar{r}_{a})(r_{u,i}-\bar{r}_{u})}{\sqrt{ \sum_{i\in I}(r_{a,i}-\bar{r}_{a})^{2}}\sqrt{\sum_{i\in I}(r_{u,i}-\bar{r}_{ u})^{2}}} \tag{1}\]  
where \(w_{a,u}\) is the similarities between user \(a\) and user \(u\). \(i\in I\) represents the summation of the items that both user \(a\) and user \(u\) have rated, \(\bar{r}_{a}\) is the average rating of the co-rated items of the user \(a\), and \(\bar{r}_{u}\) is the average rating of the co-rated items of the user \(u\). Then the recommendation score for an item is computed by a weighted combination of historical ratings on the items from the similar users shown as follows:  
\[P_{a,i}=\bar{r}_{a}+\frac{\sum_{u\in I}(r_{u,i}-\bar{r}_{u})w_{a,u}}{\sum_{u \in I}|w_{a,u}|} \tag{2}\]  
where \(P_{a,i}\) represents the prediction for the user \(a\) on a certain item \(i\). \(\bar{r}_{a}\) and \(\bar{r}_{u}\) are the average ratings for the user \(a\) and user \(u\) respectively. The summation of all the users \(u\in U\) who have rated the item \(i\). In contrast, IBCF works by finding items that are similar to other items that the target user has liked or rated. The formula is shown as follows:  
\[w_{i,n}=\frac{\sum_{u\in U}(r_{u,i}-\bar{r}_{i})(r_{u,n}-\bar{r}_{n})}{\sqrt{ \sum_{u\in U}(r_{u,i}-\bar{r}_{i})^{2}}\sqrt{\sum_{u\in U}(r_{u,n}-\bar{r}_{n} )^{2}}} \tag{3}\]Note that \(u\in U\) denotes the set of users who rated both item \(i\) and item \(n\), where \(i\) and \(n\) are indices for items. \(r_{u,i}\) is the rating of user \(u\) on item \(i\), \(r_{u,n}\) is the rating of user \(u\) on item \(n\), \(\bar{r_{i}}\) is the average rating of item \(i\) by all users. \[p_{u,i}=\frac{\sum_{n\in N}r_{u,n}\cdot w_{i,n}}{\sum_{n\in N}|w_{i,n}|} \tag{4}\]  
where \(p_{u,i}\) is the prediction of user \(u\) on item \(i\). \(n\in N\) is the summations of other rated items by user \(u\). \(w_{i,n}\) is the weight between item \(i\) and item \(n\). \(r_{u,n}\) is the rating of user \(u\) on item \(n\). Model-based CF builds models using data mining techniques, such as Bayesian networks, clustering models, and others on user ratings where the models are used to generate recommendations [17]. The model building algorithms are usually computationally expensive. Personalized mean (PersMean) is one of the techniques used in recommender system to generate recommendations. This technique operates by computing the users and the items average offsets from the global rating. It implements the prediction rule \(p_{u,i}=\mu+b_{i}+b_{u}\), where \(\mu\) is the global mean rating, \(b_{i}\) is the difference between the item's mean rating and the global mean, and \(b_{u}\) is the mean of the differences between the user's ratings for each items and the item's mean. To the best of our knowledge, there has never been any work supported by detailed experimental analysis on mixing both friends and natives based recommender system. However, there are only a handful of research that have been carried out on friend-based or native-based recommender systems. [PERSON] et al. [1] have proposed the friend-based collaborative filtering (FCF) approach and the geo-measured friend-based collaborative filtering (GM-FCF) approach. Through the authors' analysis on the dataset collected from Foursquare (a local search-and-discovery mobile app with location data service platform), they have observed a strong social and geospatial ties among users and their favorite locations or places in the system.