| The advancement of the mobile technology allows people to use social networking services to connect with each other,share contents,obtain information and make purchases.There are different social networks that recommend friends,items,jobs,movies,book,and so on.But,all of them need to constantly build credibility with the active users.These social networking services provide some kind of recommendation services which focused on the user's main preference or interest selection.The existing system takes so long to identify the right friend or person for new user to connect with others.Most websites use simple friend recommendation algorithms such as popularity,similarity,or the formula the friend's friend is my friend,which are very intuitive.After receiving a request,Friend Recommendation system goes to the database and gives a list of people with the highest recommendation scores to the query user.In this paper,we build a Friend Recommendation system for social networks which will be measure the frequency of user activities and update the dataset according to the frequency and intensity of their activities.We will be use the K-Nearest Neighbor algorithm;we can easily classify the user activity and behaviors with some criteria.We also implement a user's daily life journal,from which their life preferences are extracted by using the K-Nearest Neighbors with threshold algorithm.We use a similar method to calculate the similarity of life styles or preference between users,and determine users' impact in terms of interests with a public-private graph.We have implemented the Friend Recommendation system,and evaluated its performance on both small-scale experiments and large-scale simulations.The results show that the recommendations reflect the users' preferences in choosing friends.In this paper,we propose an innovative method for context-aware friend recommendation,which incorporates mobile computing associate to people relation in order toimprove the rating of users' preferences. |
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