Integration of social networks and recommendation of social activities (I)

In recent years, Social Networking Sites (SNSs) has become increasing popular among people. Many people also have multiple SNSs for different purposes. The increase in the huge amount of social data (friends and activities) has resulted in users spending an excessive amount of time in managing them. We propose implementing a social network aggregator, SocConnect that can simplify these social data by providing functions that increase users’ efficiency in managing social data. Our functions include categorizing these social data according to users’ preference and using different machine learning techniques on users’ rating to generate personalized recommendations. Our project would be using Primefaces for user-friendly interfaces and Waikato Environment for Knowledge Analysis (WEKA) for machine learning techniques. Through our project, users are able to look for social data more efficiently through easy navigation of user interfaces, thus wasting less time on less important social data. Through personalized recommendations of social data, users are also prevented from network overload. [1] Although the project has achieved its aim in increasing social data efficiency, it can be further improved by increasing the options of SNSs that can be integrated in the future.Bachelor of Engineering (Computer Engineering