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FRIENDBOOK: A SEMANTIC-BASED FRIEND
RECOMMENDATION SYSTEM FOR SOCIAL NETWORKS
ABSTRACT
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. By taking advantage of sensor-rich smartphones, Friendbook discovers life
styles of users from user-centric sensor data, measures the similarity of life styles between users,
and recommends friends to users if their life styles have high similarity. 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. Finally, Friendbook integrates a
feedback mechanism to further improve the recommendation accuracy. 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.

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  • 1. FRIENDBOOK: A SEMANTIC-BASED FRIEND RECOMMENDATION SYSTEM FOR SOCIAL NETWORKS ABSTRACT 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. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. 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. Finally, Friendbook integrates a feedback mechanism to further improve the recommendation accuracy. 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.