Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Efficient search ranking in social networks
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Egocentric Information Abstraction for Heterogeneous Social Networks
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
Social network document ranking
Proceedings of the 10th annual joint conference on Digital libraries
Understanding latent interactions in online social networks
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
LikeMiner: a system for mining the power of 'like' in social media networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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In recent years, social networking sites have becoming important platforms for users to establish the relationships between each other. As time goes by, the links between people will form the so-called 隆§Strong Links隆篓. For those users, information provided by the friends with strong link is considered as more interesting and useful. Most of recent search engines are designed based on only measuring the similarity between keywords and articles. However, the social relations between authors of articles and searcher have not been taken into account in recent research. Therefore, in order to improve the performance of recent search engines, we include the measurement of social relationships in search engine and expect the search quality can be improved. In this study, we collected the data from Facebook to calculate the social relationship. About the content, the data will be processed by using CKIP (Chinese word net) and TF-IDF. Finally, we combine key-word frequency and social relations as a value, which is called the Social Ranking vaule. The value will be used as the key to rank the search results. In this paper, we will also demonstrate a real example to explain the proposed methodology as well as a system interface.