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.