Friendbook A Semantic based Friend Recommendation System for Social Networks(2014)

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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 Friend book, 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 smart phones, Friend book 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, Friend book returns a list of people with highest recommendation scores to the query user. Finally, Friend book integrates a feedback mechanism to further improve the recommendation accuracy. We have implemented Friend book on the Android-based smart phones, 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, Face book 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 SPECIFICATION

Hardware Requirements:

•         System                           :   Pentium IV 3.5 GHz.

•         Hard Disk                      :   40 GB.

•         Floppy Drive                 :   1.44 Mb.

•         Monitor                          :   14’ Colour Monitor.

•         Mouse                           :   Optical Mouse.

•         Ram                               :   1 GB.

Software Requirements:

•         Operating system         :   Windows XP or Windows 7, Windows 8.

•         Coding Language       :   J2EE and Android

•         Data Base                    :   My Sql

•         Documentation           :  MS Office

•         IDE                                :   Eclipse Galileo and Juno

•         Development Kit        : JDK 1.6

Click here to download Friendbook A Semantic based Friend Recommendation System for Social Networks(2014) source code