Description

In everyday life, we seek suggestions from people we know for deciding the best place to buy a particular good or service. In this work, we put forth a framework of an automated distributed recommendation system on a social network that exploits the widely studied concept of trust , to get personalized responses. It integrates many sources of data in order to generate the relevant personalized recommendations for network members. The unique social filtering techniques and measures of the activity and strength of relationship are encompassed by the framework. Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. Inspired by text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’ impact in terms of life styles with a friend-matching graph. Upon receiving a request, Friendbook returns a list of people with highest recommendation scores to the query user. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users in choosing friends.