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 mostappropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic basedfriend recommendation system for social networks, which recommends friends to users based on their life styles instead of socialgraphs. 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. Inspiredby text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent DirichletAllocation 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 highestrecommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the recommendationaccuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on both small-scaleexperiments and large-scale simulations. The results show that the recommendations accurately reflect the preferences of users inchoosing friends.

EXISTING SYSTEM:

People typically made friends withothers who live or work close to themselves, such asneighbors or colleagues. We call friends made throughthis traditional fashion as G-friends, which stands for geographicallocation-based friends because they are influencedby the geographical distances between each other.With the rapid advances in social networks, servicessuch as Facebook, Twitter and Google+ have providedus revolutionary ways of making friends. According toFacebook statistics, a user has an average of 130 friends,perhaps larger than any other time in history.One challenge with existing social networking servicesis how to recommend a good friend to a user. Mostof them rely on pre-existing user relationships to pickfriend candidates. For example, Facebook relies on asocial link analysis among those who already sharecommon friends and recommends symmetrical usersas potential friends. Unfortunately, this approach maynot be the most appropriate based on recent sociologyfindings.

DISADVANTAGES OF EXISTING SYSTEM:

  • It does not meet the user needs.
  • It is not appropriate method to recommend friends.

PROPOSED SYSTEM:

Our proposed solution is also motivated by the recentadvances in smartphones, which have become more andmore popular in people’s lives. These smartphones (e.g.,iPhone or Android-based smartphones) are equippedwith a rich set of embedded sensors, such as GPS, accelerometer,microphone, gyroscope, and camera. Thus,a smartphone is no longer simply a communicationdevice, but also a powerful and environmental realitysensing platform from which we can extract rich contextand content-aware information. From this perspective,smartphones serve as the ideal platform for sensingdaily routines from which people’s life styles could bediscovered.In spite of the powerful sensing capabilities of smartphones,there are still multiple challenges for extractingusers’ life styles and recommending potential friendsbased on their similarities. First, how to automaticallyand accurately discover life styles from noisy and heterogeneoussensor data? Second, how to measure thesimilarity of users in terms of life styles? Third, whoshould be recommended to the user among all the friendcandidates? To address these challenges, in this paper,we present Friendbook, a semantic-based friend recommendationsystem based on sensor-rich smartphones.

ADVANTAGES OF PROPOSED SYSTEM:

  • Friendbook is the firstfriend recommendation system exploiting a user’slife style information.
  • It use the probabilistic topic model to extract lifestyle information of users.

SYSTEM ARCHITECTUTRE:

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

Processor-Pentium –IV

Speed-1.1 Ghz

RAM-512 MB(min)

Hard Disk-40 GB

Key Board-Standard Windows Keyboard

Mouse-Two or Three Button Mouse

Monitor-LCD/LED

SOFTWARE REQUIREMENTS:

•Operating system:Windows XP

•Coding Language:Java

•Data Base:MySQL

•Tool:Net Beans IDE

REFERENCE:

Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi, and Zhi Wang, “Friendbook: A Semantic-based FriendRecommendation System for Social Networks”IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 99, MAY2014.