Friendbook: A Semantic-Based Friend Recommendation System for Social Networks

TWENTY years past, folks generally created friends with others who live or work near themselves, like neighbors or colleagues. we have a tendency to decision friends created through this ancient fashion as G-friends, that stands for geographical location-based friends as a result of they're influenced by the geographical distances between one another. With the speedy advances in social networks, services like Facebook, Twitter and Google+ have provided us revolutionary ways in which of creating friends.According to Facebook statistics, a user has a mean of one hundred thirty friends, maybe larger than the other time in history. One challenge with existing social networking services is a way to suggest a good or reliable friend to a user. Most of them rely on pre-existing user relationships to choose friend candidates. for instance, Facebook depends on a social link analysis among those that already share common friends and recommends  users as potential friends.Unfortunately, this approach might not be the foremost applicable supported recent social science ï¬ndings. according to these studies, the principles to group individuals along include: 1) habits or life style; 2) attitudes; 3) tastes; 4) ethical standards; 5) economic level; and 6) individuals they already know. life styles are typically closely correlate with daily routines and activities. Therefore, if we tend to may gather data on users’ daily routines and activities, we are able to exploit rule #1 and suggest friends to individuals supported their similar life styles. This recommendation mechanism may be deployed as a standalone app on smartphones for existing social network frameworks

A Semantic-Based Friend Recommendation System for Large-scale System

Informal community locales have pulled in a large number of clients with the social revolution in Web 2.0 . Most informal organization sites depend on individuals' vicinity on the social diagram for friends suggestion. Existing work have a tendency to present Match Maker, a cooperative filtering friend recommendation system supported temperament matching. The goal of Match Maker is to leverage the social data and mutual affection among individuals in existing social network connections,and turn out friend recommendations supported made discourse information from people’s physical world interactions. Matcher permits users’ network to match them with similar TV characters, and uses relationships within the TV programs as parallel comparison matrix to recommend to the users friends that are voted to suit their temperament the most effective. The system’s ranking schema permits progressive improvement on the temperament matching, accord and a lot of various branching of users’ social network connections. Lastly, our user study shows that the appliance can even induce a lot of TV content consumption by driving users’ curiosity within the ranking method

An Information Diffusion-Based Recommendation Framework for Micro-Blogging

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches

Personalizing Recommendation in Micro-blog Social Networks and E-Commerce

Ph.DDOCTOR OF PHILOSOPH

An evaluation of identity in online social networking: distinguishing fact from fiction

Online social networks are understood to replicate the real life connections between people. As the technology matures, more people are joining social networking communities such as MySpace (www.myspace.com) and Facebook (www.facebook.com). These online communities provide the opportunity for individuals to present themselves and maintain social interactions through their profiles. Such traces in profiles can be used as evidence in deciding the level of trust with which to imbue individuals in making access control decisions. However, online profiles have serious implications over the reality of identity disclosure. There are many reasons why someone may choose not to reveal their true self, which sometimes leads to misidentification or deception. On one hand, the structure of online profiles allows anonymity, which gives users the opportunity to create a persona that may not represent their true identity. On the other hand, we often play multiple identities in different contexts where such behaviour is acceptable. However, realizing the context for each identity representation depends on the individual. As a result, some represented identities will be essentially real, if edited for public view, some will be disguised, and others will be fictitious or humorous. The millions of social network profiles, and billions of connections between them, make it difficult to formalize an automated approach to differentiate fact from fiction in online self-described identities. How can we be sure with whom we are interacting, and whether these individuals or groups are being truthful with the online identities they present to the rest of the community? What tools and techniques can be used to gather, organize, and explore the available data for informing the level of honesty that should be entrusted to an individual? Can we verify the validity of the identity automatically, based on the available information online? We aim to evaluate identity representation online and examine how identity can be verified in a less trusted online community. We propose a personality classifier model to identify a user‟s personality (such as expressive, valid, active, positive, popular, sociable and traceable) using traces of 2.2 million profile features collected from MySpace. We use data mining techniques and social network analysis to extract significant patterns in the data and network structure, and improve the classifier during the cycle of development. We evaluate our classifier model on profiles with known identities such as „real‟ and „fake‟. Our results indicate that by utilizing people‟s online, self-reported information, personality, and their network of friends and interactions, we are able to provide evidence for validating the type of identity in a manner that is both accurate and scalable