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.
EXISTING SYSTEM:
Most of the
friend suggestions mechanism relies on pre-existing user relationships to pick
friend candidates. For example, Facebook relies on a social link analysis among
those who already share common friends and recommends symmetrical users as
potential friends. The rules to group people together include:
1) Habits or life style
2) Attitudes
3) Tastes
4) Moral standards
5) Economic level; and
6) People they already know.
Apparently, rule
#3 and rule #6 are the mainstream factors considered by existing recommendation
systems.
DISADVANTAGES
OF EXISTING SYSTEM:
Ø 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
PROPOSED SYSTEM:
Ø A novel semantic-based friend recommendation system
for social networks, which recommends friends to users based on their life
styles instead of social graphs.
Ø 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.
Ø 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.
Ø Similarity metric to measure the similarity of life
styles between users, and calculate users’
Ø Impact in terms of life styles with a friend-matching
graph.
Ø We integrate a linear feedback mechanism that exploits
the user’s feedback to improve recommendation accuracy.
ADVANTAGES
OF PROPOSED SYSTEM:
Ø Recommendeds potential friends to users if they share
similar life styles.
Ø The feedback mechanism allows us to measure the
satisfaction of users, by providing a user interface that allows the user to
rate the friend list
SYSTEM
REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø
System : Pentium IV 2.4 GHz.
Ø
Hard Disk :
40 GB.
Ø
Floppy Drive : 1.44
Mb.
Ø
Monitor : 15
VGA Colour.
Ø
Mouse :
Logitech.
Ø Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
Ø Operating system : Windows
XP/7.
Ø Coding Language : JAVA/J2EE
Ø IDE : Netbeans 7.4
Ø Database : MYSQL
REFERENCE:
Zhibo Wang,
Jilong Liao, Qing Cao, Hairong Qi, and Zhi Wang, “Friendbook: A Semantic-based Friend Recommendation System for Social
Networks”, IEEE TRANSACTIONS ON
MOBILE COMPUTING, 2014
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