Title :
A Rule-Based Recommendation for Personalization in Social Networks
Author :
Rui Zhang ; Yueqi Zhou ; Lin Li ; Chengming Zou
Author_Institution :
Hubei Key Lab. of Transp. Internet of Things, Wuhan Univ. of Technol., Wuhan, China
Abstract :
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.
Keywords :
recommender systems; social networking (online); interactive behaviors; online social networks; personal data; recommendation strategies; rule based method; rule based recommendation; user profiles; Arrays; Atomic measurements; Computer science; Filtering; Motion pictures; Social network services; Transportation; personalized recommendation; rule-based; social network;
Conference_Titel :
Services Computing Conference (APSCC), 2014 Asia-Pacific
DOI :
10.1109/APSCC.2014.12
