A Rule-Based Recommendation for Personalization in Social Networks

All online social networks gather data that reflects users' profiles, interactive behaviors and shared activities. This data can be used to extract users' interests and make recommendations. According to abundant personal data, recommenders can identify information relevant for individuals. To reveal users' different preferences explicitly, we present a rule-based method which supports different recommendation strategies. Moreover, we also show that this method is effective by conducting experiments on real data.