Towards Effective Recommendation of Social Data across Social Networking Sites

Abstract

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter, are often overwhelmed by the huge amount of social data (friends’ updates and other activities). We propose using machine learning techniques to learn preferences of users and generate personalized recommendations. We apply four different machine learning techniques on previously rated activities and friends to generate personalized recommendations for activities that may be interesting to each user. We also use different non-textual and textual features to represent activities. The evaluation results show that good performance can be achieved when both non-textual and textual features are used, thus helping users deal with cognitive overload.