Personalized Recommendation of Integrated Social Data across Social Networking Sites

We have developed a dashboard application called “SoCConnect” for integrating social data from different social networking sites (e.g. Facebook, Twitter), which allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. We compare the results of applying five different machine learning techniques on previously rated activities and friends to generate personalized recommendations for activities that may be interesting to each user. The results show that machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with cognitive overload. A visualization technique that has been shown to work well in previous work is applied to display personalized recommendations.