70
A SEMANTIC-BASED FRIEND RECOMMENDATION
SYSTEM FOR SOCIAL NETWORKS
Ms. P. KALAIYARASI, M.E. Scholar, Mr. M. S. ARUN KUMAR, M.E., (Ph.D)., Assistant Professor,
Department of Computer Science & Engineering,
Surya Engineering College, Mettukadai, Erode, Tamil Nadu, India
ABSTRACT
A mobile social network is a social network where people with common interests meet and converse using a mobile phone or a tablet. Mobile social networks make use of mobile messaging applications and are considered one of the best ways for providing a smoother user interaction and also for engaging users. 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.
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. 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.
1. Introduction
Mobile social networking is social networking where individuals with similar interests converse and connect with one another through their mobile phone and/or tablet. Much like web-based social networking, mobile social networking occurs in virtual communities. A current trend for social networking websites, such as Facebook, is to create mobile apps to give their users instant and real-time access from their device. In turn, native mobile social networks have been created
71 for mobile access through mobile browsers and smartphone apps, and 2) Native mobile social networks with dedicated focus on mobile use like mobile communication, location-based services, and augmented reality, requiring mobile devices and technology. Mobile and web-based social networking systems often work symbiotically to spread content, increase accessibility and connect users.
2. Related Work
2.1. Latent Dirichlet allocation
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variation methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.
2.2. Discovering routines from large scale human
locations using probabilistic topic models
In this work we discover the daily location-driven routines which are contained in a massive real life human dataset collected by mobile phones.
Our goal is the discovery and analysis of human routines which characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16 month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group’s activities, identified.
2.3. Probabilistic Mining of Socio-Geographic
Routines from Mobile Phone Data
There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper, we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, can be integrated with human location data, obtained by mobile cell tower connections, to mine meaningful details about human activities from large and noisy datasets.
We propose a model, called bag of multimodal behavior that integrates the modeling of variations of location over multiple time-scales, and the modeling of interaction types from proximity. We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately a 10-month period using data from MIT’s Reality Mining project. Some of the human
72 representation include “going out from 7 pm– midnight alone” and “working from 11 am–5 pm
with 3–5 other people,” further finding that this activity dominantly occurs on specific days of the week. Our methodology also finds dominant work patterns occurring on other days of the week. We further demonstrate the feasibility of the topic modeling framework for human routine discovery by predicting missing multimodal phone data at specific times of the day.
3. Friends Recommendation Scheme
Twenty years ago, people typically made friends with others who live or work close to themselves, such as neighbors or colleagues. We call friends made through this traditional fashion as G-friends, which stands for geographical location-based friends because they are influenced by the geographical distances between each other. With the rapid advances in social networks, services such as Facebook, Twitter and Google+ have provided us revolutionary ways of making friends. According to Facebook statistics, a user has an average of 130 friends, perhaps larger than any other time in history. One challenge with existing social networking services is how to recommend a good friend to a user. Most of them rely 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. Unfortunately, this approach may not be the most appropriate based on recent sociology findings. According to these studies, 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 and rule 6are the main stream factors considered by existing recommendation systems. Rule 1, although probably the most intuitive, is not widely used because users’ life
styles are difficult, if not impossible, to capture through web actions. Rather, life styles are usually closely correlated with daily routines and activities. Therefore, if we could gather information on users’
daily routines and activities, we can exploit rule 1and recommend friends to people based on their similar life styles. This recommendation mechanism can be deployed as a standalone app on smartphones or as an add-on to existing social network frameworks.
73 are lifestyles in our study. Just like words serve as the basis of documents, people’s activities naturally
serve as the primitive Vocabulary of these life documents.
Our proposed solution is also motivated by the recent advances in smartphones, which have become more and more popular in people’s lives. These smartphone are equipped with a rich set of embedded sensors, such as GPS, accelerometer, microphone, gyroscope, and camera. Thus, a smartphone is no longer simply a communication device, but also a powerful and environmental reality sensing platform from which we can extract rich context and content-aware information. From this perspective, smartphones serve as the ideal platform for sensing daily routines from which people’s lifestyles could be discovered. In spite of
the powerful sensing capabilities of smartphones, there are still multiple challenges for extracting users’ life styles and recommending potential
friends based on their similarities. Second, how to measure the similarity of users in terms of lifestyles? Third, who should be recommended to the user among all the friend candidates. To address these challenges, in this paper, we present Friendbook, a semantic-based friend recommendation system based on sensor-rich smartphones.
3.1. The contributions of this work are
summarized as follows:
To the best of our knowledge, Friendbook is the first friend recommendation system exploiting a user’s lifestyle information discovered from
smartphone sensors. Inspired by achievements in the field of text mining, we model the daily lives of users as life documents and use the probabilistic topic model to extract lifestyle information of users. We propose a unique similarity metric to characterize the similarity of users in terms of life styles and then construct a friend-matching graph to recommend friends to users based on their life styles. We integrate a linear feedback mechanism that exploits the user’s feedback to improve
recommendation accuracy. 4. Problem Statement
74 leaking of their personal information and their location privacy. 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. The mobile users may face the risk of leaking of their personal information and their location privacy. The existing apps fail to consider hide of users’ profiles.
5. Privacy Persevered Friends Recommendation
for Mobile Social Networks
5.1. Privacy Preserving Module
Mobile online social networks have gained tremendous momentum in the recent years due to both the wide proliferation of mobile devices such as smartphones and tablets as well as the ubiquitous availability of network services. We present a novel Privacy Preserving and Fairness aware Friend Matching Protocol. In the designed protocol, a successful matching only happens in case that the interests of both of the participants could match the profiles of the others. In other words, no one can learn any extra information from the protocol unless another participant is exactly what he is looking for. 5.2. Secure Friend Discovery In Mobile Social
Network Module
The profile matching directly considers Alice has her personal profile, which includes three attributes: age, girl and movie. She is interested in finding a boy with similar age and hobbies. Conversely, Bob also has his own profile and interests. A successful matching could be achieved in case that Alice’s profile matches Bob’s interest
while, at the same time, Bob’s profile matches Alice’s interest. Such a mapping process could be
well supported by the existing online dating social networks, in which a member may seek another member satisfying some particular requirements. Further, the existing proposals are one-way only and profile matching requires running a protocol twice, with reversed roles in the second run.
5.3. Fairness Aware Friend Matching Protocol
Module
For the first time, we separate the user’s
interest from its profile, which is expected to be a generalization of traditional profile matching problem. We introduce a novel blind vector transformation technique, which could hide the correlation between the original vector and the transformed result. Based on it, we propose the privacy-preserving and fairness-aware friend matching protocol, which enables one party to match its interest with the profile of another, and vice versa, without revealing their real interest. We introduce a novel lightweight verifier checking approach to thwart runaway attack and thus achieve the fairness of two participants. We implement our protocols in real experiments. We demonstrate the performance of the proposed scheme via extensive experiment results.
5.4. Blind Transformation Protocol Module
75 parties to transform two vectors into the blind ones by following a series of private and identical steps, e.g., adding a random vector, shuffling in the same order. Since the transformation follows the same step, the matching results.
6. Conclusion
In this paper we have presented a, A Semantic-Based Friend Recommendation System for Social Networks, developed for Android using Java, MySql Server. The main aim of the friendbook is to recommends friends to users based on their life styles instead of social graphs. 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.
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