Contextualization in Large Scale Social Networks

Social computing-based applications provide a coherent medium through which people can be interactive and socialize by developing a Web-based communication channel that integrates different Social Networking Services (SNSs) in the Social Networking Platforms (SNPs). Different SNSs, such as photo, audio, and video sharing, have emerged as an essential resources for the dissemination of information about the human interaction patterns. Most of the SNSs are integrated into a comprehensive and coherent paradigm called the Social Networking Platform (SNP). Most of the existing SNSs focused on content-based, media-based, and geo-location-based approach. The content-based SNSs allow the text-based interactions among individuals, such as communities, blogs, and social news. The media-based SNSs provide the social interaction through various multimedia formats, such as video and audio. Geo-location-based SNSs provide location-based social communication. However, all of the aforementioned techniques lack the semantic analysis which is the most integral and crucial part of the true understanding. The goal of this dissertation is to incorporate the existing SNSs into the context-enriched information that provide the services customization based on the individual human characteristics, such as human preferences, and emotions. The computer interactive infrastructure can be enriched by leveraging information about the users? personal context (profile, preferences, attitude, and habits) that provides sophisticated context-aware services, such as semantic-based search and context-aware recommendations. The dissertation proposes MobiContext, a cloud-based Bi-Objective Recommendation Framework (BORF) for mobile social networks that generates real-time recommendation of venues for a group of mobile users. The MobiContext utilizes multi-objective optimization techniques to generate personalized recommendations. To address the issues pertaining to cold start and data sparseness, the BORF performs data preprocessing by using the Hub-Average (HA) inference model. Moreover, the Weighted Sum Approach (WSA) is implemented for scalar optimization and an evolutionary algorithm (NSGA-II) is applied for vector optimization to provide optimal suggestions to the users about a venue. The dissertation also proposes a SocialRec, a context-aware recommendation framework that utilizes a rating sentiment inference approach to incorporate textual users? review into traditional collaborative filtering methods for personalized recommendations. The proposed framework utilizes semantic analysis scores on the users? contextual information to produce optimal recommendations.Electrical and Computer EngineeringElectrical and Computer EngineeringCollege of Engineerin